Physics-based deformable organisms for medical image analysis

NASA Astrophysics Data System (ADS)

Hamarneh, Ghassan; McIntosh, Chris

2005-04-01

Previously, "Deformable organisms" were introduced as a novel paradigm for medical image analysis that uses artificial life modelling concepts. Deformable organisms were designed to complement the classical bottom-up deformable models methodologies (geometrical and physical layers), with top-down intelligent deformation control mechanisms (behavioral and cognitive layers). However, a true physical layer was absent and in order to complete medical image segmentation tasks, deformable organisms relied on pure geometry-based shape deformations guided by sensory data, prior structural knowledge, and expert-generated schedules of behaviors. In this paper we introduce the use of physics-based shape deformations within the deformable organisms framework yielding additional robustness by allowing intuitive real-time user guidance and interaction when necessary. We present the results of applying our physics-based deformable organisms, with an underlying dynamic spring-mass mesh model, to segmenting and labelling the corpus callosum in 2D midsagittal magnetic resonance images.

Label-Free Molecular Imaging of Biological Cells and Tissues by Linear and Nonlinear Raman Spectroscopic Approaches.

PubMed

Krafft, Christoph; Schmitt, Michael; Schie, Iwan W; Cialla-May, Dana; Matthäus, Christian; Bocklitz, Thomas; Popp, Jürgen

2017-04-10

Raman spectroscopy is an emerging technique in bioanalysis and imaging of biomaterials owing to its unique capability of generating spectroscopic fingerprints. Imaging cells and tissues by Raman microspectroscopy represents a nondestructive and label-free approach. All components of cells or tissues contribute to the Raman signals, giving rise to complex spectral signatures. Resonance Raman scattering and surface-enhanced Raman scattering can be used to enhance the signals and reduce the spectral complexity. Raman-active labels can be introduced to increase specificity and multimodality. In addition, nonlinear coherent Raman scattering methods offer higher sensitivities, which enable the rapid imaging of larger sampling areas. Finally, fiber-based imaging techniques pave the way towards in vivo applications of Raman spectroscopy. This Review summarizes the basic principles behind medical Raman imaging and its progress since 2012. © 2017 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Desiderata for product labeling of medical expert systems.

PubMed

Geissbühler, A; Miller, R A

1997-12-01

The proliferation and increasing complexity of medical expert systems raise ethical and legal concerns about the ability of practitioners to protect their patients from defective or misused software. Appropriate product labeling of expert systems can help clinical users to understand software indications and limitations. Mechanisms of action and knowledge representation schema should be explained in layperson's terminology. User qualifications and resources available for acquiring the skills necessary to understand and critique the system output should be listed. The processes used for building and maintaining the system's knowledge base are key determinants of the product's quality, and should be carefully documented. To meet these desiderata, a printed label is insufficient. The authors suggest a new, more active, model of product labeling for medical expert systems that involves embedding 'knowledge of the knowledge base', creating user-specific data, and sharing global information using the Internet.

Machine learning for medical images analysis.

PubMed

Criminisi, A

2016-10-01

This article discusses the application of machine learning for the analysis of medical images. Specifically: (i) We show how a special type of learning models can be thought of as automatically optimized, hierarchically-structured, rule-based algorithms, and (ii) We discuss how the issue of collecting large labelled datasets applies to both conventional algorithms as well as machine learning techniques. The size of the training database is a function of model complexity rather than a characteristic of machine learning methods. Crown Copyright © 2016. Published by Elsevier B.V. All rights reserved.

Advances in PET myocardial perfusion imaging: F-18 labeled tracers.

PubMed

Rischpler, Christoph; Park, Min-Jae; Fung, George S K; Javadi, Mehrbod; Tsui, Benjamin M W; Higuchi, Takahiro

2012-01-01

Coronary artery disease and its related cardiac disorders represent the most common cause of death in the USA and Western world. Despite advancements in treatment and accompanying improvements in outcome with current diagnostic and therapeutic modalities, it is the correct assignment of these diagnostic techniques and treatment options which are crucial. From a diagnostic standpoint, SPECT myocardial perfusion imaging (MPI) using traditional radiotracers like thallium-201 chloride, Tc-99m sestamibi or Tc-99m tetrofosmin is the most utilized imaging technique. However, PET MPI using N-13 ammonia, rubidium-82 chloride or O-15 water is increasing in availability and usage as a result of the growing number of medical centers with new-generation PET/CT systems taking advantage of the superior imaging properties of PET over SPECT. The routine clinical use of PET MPI is still limited, in part because of the short half-life of conventional PET MPI tracers. The disadvantages of these conventional PET tracers include expensive onsite production and inconvenient on-scanner tracer administration making them unsuitable for physical exercise stress imaging. Recently, two F-18 labeled radiotracers with longer radioactive half-lives than conventional PET imaging agents have been introduced. These are flurpiridaz F 18 (formerly known as F-18 BMS747158-02) and F-18 fluorobenzyltriphenylphosphonium. These longer half-life F-18 labeled perfusion tracers can overcome the production and protocol limitations of currently used radiotracers for PET MPI.

NEED FOR HARMONIZATION OF LABELING OF MEDICAL DEVICES: A REVIEW

PubMed Central

Songara, Raiendra K.; Sharma, Ganesh N.; Gupta, Vipul K.; Gupta, Promila

2010-01-01

Medical device labeling is any information associated with a device targeted to the patient or lay caregiver. It is intended to help assure that the device is used safely and effectively. Medical device labeling is supplied in many formats, for example, as patient brochures, patient leaflets, user manuals, and videotapes. The European commission has discussed a series of agreements with third countries, Australia, New Zealand, USA, Canada, Japan and Eastern European countries wishing to join the EU, concerning the mutual acceptance of inspection bodies, proof of conformity in connection with medical devices. Device labeling is exceedingly difficult for manufacturers for many reasons like regulations from government bodies to ensure compliance, increased competent authority surveillance, increased audits and language requirements. PMID:22247840

HCP: A Flexible CNN Framework for Multi-label Image Classification.

PubMed

Wei, Yunchao; Xia, Wei; Lin, Min; Huang, Junshi; Ni, Bingbing; Dong, Jian; Zhao, Yao; Yan, Shuicheng

2015-10-26

Convolutional Neural Network (CNN) has demonstrated promising performance in single-label image classification tasks. However, how CNN best copes with multi-label images still remains an open problem, mainly due to the complex underlying object layouts and insufficient multi-label training images. In this work, we propose a flexible deep CNN infrastructure, called Hypotheses-CNN-Pooling (HCP), where an arbitrary number of object segment hypotheses are taken as the inputs, then a shared CNN is connected with each hypothesis, and finally the CNN output results from different hypotheses are aggregated with max pooling to produce the ultimate multi-label predictions. Some unique characteristics of this flexible deep CNN infrastructure include: 1) no ground-truth bounding box information is required for training; 2) the whole HCP infrastructure is robust to possibly noisy and/or redundant hypotheses; 3) the shared CNN is flexible and can be well pre-trained with a large-scale single-label image dataset, e.g., ImageNet; and 4) it may naturally output multi-label prediction results. Experimental results on Pascal VOC 2007 and VOC 2012 multi-label image datasets well demonstrate the superiority of the proposed HCP infrastructure over other state-of-the-arts. In particular, the mAP reaches 90.5% by HCP only and 93.2% after the fusion with our complementary result in [44] based on hand-crafted features on the VOC 2012 dataset.

An investigative model evaluating how consumers process pictorial information on nonprescription medication labels.

PubMed

Sansgiry, S S; Cady, P S

1997-01-01

Currently, marketed over-the-counter (OTC) medication labels were simulated and tested in a controlled environment to understand consumer evaluation of OTC label information. Two factors, consumers' age (younger and older adults) and label designs (picture-only, verbal-only, congruent picture-verbal, and noncongruent picture-verbal) were controlled and tested to evaluate consumer information processing. The effects exerted by the independent variables, namely, comprehension of label information (understanding) and product evaluations (satisfaction, certainty, and perceived confusion) were evaluated on the dependent variable purchase intention. Intention measured as purchase recommendation was significantly related to product evaluations and affected by the factor label design. Participants' level of perceived confusion was more important than actual understanding of information on OTC medication labels. A Label Evaluation Process Model was developed which could be used for future testing of OTC medication labels.

Novel image processing method study for a label-free optical biosensor

NASA Astrophysics Data System (ADS)

Yang, Chenhao; Wei, Li'an; Yang, Rusong; Feng, Ying

2015-10-01

Optical biosensor is generally divided into labeled type and label-free type, the former mainly contains fluorescence labeled method and radioactive-labeled method, while fluorescence-labeled method is more mature in the application. The mainly image processing methods of fluorescent-labeled biosensor includes smooth filtering, artificial gridding and constant thresholding. Since some fluorescent molecules may influence the biological reaction, label-free methods have been the main developing direction of optical biosensors nowadays. The using of wider field of view and larger angle of incidence light path which could effectively improve the sensitivity of the label-free biosensor also brought more difficulties in image processing, comparing with the fluorescent-labeled biosensor. Otsu's method is widely applied in machine vision, etc, which choose the threshold to minimize the intraclass variance of the thresholded black and white pixels. It's capacity-constrained with the asymmetrical distribution of images as a global threshold segmentation. In order to solve the irregularity of light intensity on the transducer, we improved the algorithm. In this paper, we present a new image processing algorithm based on a reflectance modulation biosensor platform, which mainly comprises the design of sliding normalization algorithm for image rectification and utilizing the improved otsu's method for image segmentation, in order to implement automatic recognition of target areas. Finally we used adaptive gridding method extracting the target parameters for analysis. Those methods could improve the efficiency of image processing, reduce human intervention, enhance the reliability of experiments and laid the foundation for the realization of high throughput of label-free optical biosensors.

Predesigned labels to prevent medication errors in hospitalized patients: a quasi-experimental design study.

PubMed

Morales-González, María Fernanda; Galiano Gálvez, María Alejandra

2017-09-08

Our institution implemented the use of pre-designed labeling of intravenous drugs and fluids, administration routes and infusion pumps of to prevent medication errors. To evaluate the effectiveness of predesigned labeling in reducing medication errors in the preparation and administration stages of prescribed medication in patients hospitalized with invasive lines, and to characterize medication errors. This is a pre/post intervention study. Pre-intervention group: invasively administered dose from July 1st to December 31st, 2014, using traditional labeling (adhesive paper handwritten note). Post-intervention group: dose administered from January 1st to June 30th, 2015, using predesigned labeling (labeling with preset data-adhesive labels, color- grouped by drugs, labels with colors for invasive lines). Outcome: medication errors in hospitalized patients, as measured with notification form and record electronics. Tabulation/analysis Stata-10, with descriptive statistics, hypotheses testing, estimating risk with 95% confidence. In the pre-intervention group, 5,819 doses of drugs were administered invasively in 634 patients. Error rate of 1.4 x 1,000 administrations. The post-intervention group of 1088 doses comprised 8,585 patients with similar routes of administration. The error rate was 0.3 x 1,000 (p = 0.034). Patients receiving medication through an invasive route who did not use predesigned labeling had 4.6 times more risk of medication error than those who had used predesigned labels (95% CI: 1.25 to 25.4). The adult critically ill patient unit had the highest proportion of medication errors. The most frequent error was wrong dose administration. 41.2% produced harm to the patient. The use of predesigned labeling in invasive lines reduces errors in medication in the last two phases: preparation and administration.

Stigmatization of 'psychiatric label' by medical and non-medical students.

PubMed

Totic, Sanja; Stojiljkovic, Dragan; Pavlovic, Zorana; Zaric, Nenad; Zarkovic, Boris; Malic, Ljubica; Mihaljevic, Marina; Jasovic-Gasic, Miroslava; Maric, Nadja P

2012-09-01

Stigmatization of psychiatric patients is present both in the general population and among healthcare professionals. To determine the attitudes and behaviour of medical students towards a person who goes to a psychiatrist, before and after psychiatric rotation, and to compare those attitudes between medical and non-medical students. The study included 525 medical students (second and sixth year of studies) and 154 students of law. The study instrument was a three-part self-reported questionnaire (socio-demographic data, Rosenberg Self-Esteem Scale and a vignette depicting a young, mentally healthy person). The experimental intervention consisted of ascribing a 'psychiatric label' to only one set of vignettes. All the vignettes (with or without the 'psychiatric label') were followed by 14 statements addressing the acceptance of a person described by vignette, as judged by social distance (four-point Likert scale). Higher tendency to stigmatize was found in medical students in the final year, after psychiatric rotation (Z(U) = -3.12, p = .002), particularly in a closer relationship (Z(U) = -2.67, p = .007) between a student and a hypothetical person who goes to a psychiatrist. The non-medical students had a similar tendency to stigmatize as medical students before psychiatric rotation (Z(U) = -0.03, p = .975). Neither gender, nor the size of student's place of origin or average academic mark was associated with the tendency to stigmatize in our sample. However, student's elf-esteem was lower in those with a tendency to stigmatize more in a distant relationship (ρ = -0.157, p = .005). Psychiatric education can either reinforce stigmatization or reduce it. Therefore, detailed analyses of educational domains that reinforce stigma will be the starting point for anti-stigma action.

Image labeling. The need for a better look.

PubMed

Hunter, T

1994-10-01

The important message in this editorial is for radiologists to critically examine how well images are labeled in their own department. If it is not satisfactory, then institute corrective measures. These can range from sophisticated computer programs for printing flashcards to merely sending the chief technologist all those films one comes across with unreadable labels. The quality of the image labeling should also be a consideration when purchasing CT, MRI, ultrasound, computed radiography and digital angiography equipment. The fact that you consider this important should be communicated to equipment manufacturers in the hope that they will pay more attention to it and offer more flexibility for each department to design its own labels. In any event, I feel consistently bad film labeling results in sloppy radiology with possible patient harm and unpleasant legal consequences for the radiologist.

Consumer involvement: effects on information processing from over-the-counter medication labels.

PubMed

Sansgiry, S S; Cady, P S; Sansgiry, S

2001-01-01

The objective of this study was to evaluate the effects of consumer involvement on information processing from over-the-counter (OTC) medication labels. A sample of 256 students evaluated simulated OTC product labels for two product categories (headache and cold) in random order. Each participant evaluated labels after reading a scenario to simulate high and low involvement respectively. A questionnaire was used to collect data on variables such as label comprehension, attitude-towards-product label, product evaluation, and purchase intention. The results indicate that when consumers are involved in their purchase of OTC medications they are significantly more likely to understand information from the label and evaluate it accordingly. However, involvement does not affect attitude-towards-product label nor does it enhance purchase intention.

Fusing Continuous-Valued Medical Labels Using a Bayesian Model.

PubMed

Zhu, Tingting; Dunkley, Nic; Behar, Joachim; Clifton, David A; Clifford, Gari D

2015-12-01

With the rapid increase in volume of time series medical data available through wearable devices, there is a need to employ automated algorithms to label data. Examples of labels include interventions, changes in activity (e.g. sleep) and changes in physiology (e.g. arrhythmias). However, automated algorithms tend to be unreliable resulting in lower quality care. Expert annotations are scarce, expensive, and prone to significant inter- and intra-observer variance. To address these problems, a Bayesian Continuous-valued Label Aggregator (BCLA) is proposed to provide a reliable estimation of label aggregation while accurately infer the precision and bias of each algorithm. The BCLA was applied to QT interval (pro-arrhythmic indicator) estimation from the electrocardiogram using labels from the 2006 PhysioNet/Computing in Cardiology Challenge database. It was compared to the mean, median, and a previously proposed Expectation Maximization (EM) label aggregation approaches. While accurately predicting each labelling algorithm's bias and precision, the root-mean-square error of the BCLA was 11.78 ± 0.63 ms, significantly outperforming the best Challenge entry (15.37 ± 2.13 ms) as well as the EM, mean, and median voting strategies (14.76 ± 0.52, 17.61 ± 0.55, and 14.43 ± 0.57 ms respectively with p < 0.0001). The BCLA could therefore provide accurate estimation for medical continuous-valued label tasks in an unsupervised manner even when the ground truth is not available.

Dissemination of information on the off-label (unapproved) use of medication: a comparative analysis.

PubMed

Jansen, Rita-Marié

2011-03-01

"Off-label" in relation to the use of medication means that a medicine is used in another way or for indications other than those specified in its conditions of registration and reflected in its labelling. The off-label use of medication accounts for an estimated 21 per cent of drug use overall and is an important part of mainstream, legitimate medical practice worldwide. In South Africa, legislation prohibits the dissemination of information regarding the off-label use of medication. There are diverging views on whether pharmaceutical companies should be allowed to distribute scientific publications on off-label uses of approved drugs. Current policy in the United States of America (USA) eases restrictions on the dissemination of information of this nature. The prohibitions existing in South Africa, however, are more comparable with those in European countries. After analysing the different legal positions on the issue, it is submitted that pharmaceutical companies should not be allowed to disseminate information on off-label uses, but that the regulatory authority play an active and leading role in providing the latest, objective medical and scientific information, as well as guidelines on the off-label use of medication. Other related recommendations are also made.

Medical Image Databases

PubMed Central

Tagare, Hemant D.; Jaffe, C. Carl; Duncan, James

1997-01-01

Abstract Information contained in medical images differs considerably from that residing in alphanumeric format. The difference can be attributed to four characteristics: (1) the semantics of medical knowledge extractable from images is imprecise; (2) image information contains form and spatial data, which are not expressible in conventional language; (3) a large part of image information is geometric; (4) diagnostic inferences derived from images rest on an incomplete, continuously evolving model of normality. This paper explores the differentiating characteristics of text versus images and their impact on design of a medical image database intended to allow content-based indexing and retrieval. One strategy for implementing medical image databases is presented, which employs object-oriented iconic queries, semantics by association with prototypes, and a generic schema. PMID:9147338

Consumer opinion on social policy approaches to promoting positive body image: Airbrushed media images and disclaimer labels.

PubMed

Paraskeva, Nicole; Lewis-Smith, Helena; Diedrichs, Phillippa C

2017-02-01

Disclaimer labels on airbrushed media images have generated political attention and advocacy as a social policy approach to promoting positive body image. Experimental research suggests that labelling is ineffective and consumers' viewpoints have been overlooked. A mixed-method study explored British consumers' ( N = 1555, aged 11-78 years) opinions on body image and social policy approaches. Thematic analysis indicated scepticism about the effectiveness of labelling images. Quantitatively, adults, although not adolescents, reported that labelling was unlikely to improve body image. Appearance diversity in media and reorienting social norms from appearance to function and health were perceived as effective strategies. Social policy and research implications are discussed.

Automatic multi-label annotation of abdominal CT images using CBIR

NASA Astrophysics Data System (ADS)

Xue, Zhiyun; Antani, Sameer; Long, L. Rodney; Thoma, George R.

2017-03-01

We present a technique to annotate multiple organs shown in 2-D abdominal/pelvic CT images using CBIR. This annotation task is motivated by our research interests in visual question-answering (VQA). We aim to apply results from this effort in Open-iSM, a multimodal biomedical search engine developed by the National Library of Medicine (NLM). Understanding visual content of biomedical images is a necessary step for VQA. Though sufficient annotational information about an image may be available in related textual metadata, not all may be useful as descriptive tags, particularly for anatomy on the image. In this paper, we develop and evaluate a multi-label image annotation method using CBIR. We evaluate our method on two 2-D CT image datasets we generated from 3-D volumetric data obtained from a multi-organ segmentation challenge hosted in MICCAI 2015. Shape and spatial layout information is used to encode visual characteristics of the anatomy. We adapt a weighted voting scheme to assign multiple labels to the query image by combining the labels of the images identified as similar by the method. Key parameters that may affect the annotation performance, such as the number of images used in the label voting and the threshold for excluding labels that have low weights, are studied. The method proposes a coarse-to-fine retrieval strategy which integrates the classification with the nearest-neighbor search. Results from our evaluation (using the MICCAI CT image datasets as well as figures from Open-i) are presented.

The effect of illustrations on patient comprehension of medication instruction labels.

PubMed

Hwang, Stephen W; Tram, Carolyn Q N; Knarr, Nadia

2005-06-16

Labels with special instructions regarding how a prescription medication should be taken or its possible side effects are often applied to pill bottles. The goal of this study was to determine whether the addition of illustrations to these labels affects patient comprehension. Study participants (N = 130) were enrolled by approaching patients at three family practice clinics in Toronto, Canada. Participants were asked to interpret two sets of medication instruction labels, the first with text only and the second with the same text accompanied by illustrations. Two investigators coded participants' responses as incorrect, partially correct, or completely correct. Health literacy levels of participants were measured using a validated instrument, the REALM test. All participants gave a completely correct interpretation for three out of five instruction labels, regardless of whether illustrations were present or not. For the two most complex labels, only 34-55% of interpretations of the text-only version were completely correct. The addition of illustrations was associated with improved performance in 5-7% of subjects and worsened performance in 7-9% of subjects. The commonly-used illustrations on the medication labels used in this study were of little or no use in improving patients' comprehension of the accompanying written instructions.

The effect of illustrations on patient comprehension of medication instruction labels

PubMed Central

Hwang, Stephen W; Tram, Carolyn QN; Knarr, Nadia

2005-01-01

Background Labels with special instructions regarding how a prescription medication should be taken or its possible side effects are often applied to pill bottles. The goal of this study was to determine whether the addition of illustrations to these labels affects patient comprehension. Methods Study participants (N = 130) were enrolled by approaching patients at three family practice clinics in Toronto, Canada. Participants were asked to interpret two sets of medication instruction labels, the first with text only and the second with the same text accompanied by illustrations. Two investigators coded participants' responses as incorrect, partially correct, or completely correct. Health literacy levels of participants were measured using a validated instrument, the REALM test. Results All participants gave a completely correct interpretation for three out of five instruction labels, regardless of whether illustrations were present or not. For the two most complex labels, only 34–55% of interpretations of the text-only version were completely correct. The addition of illustrations was associated with improved performance in 5–7% of subjects and worsened performance in 7–9% of subjects. Conclusion The commonly-used illustrations on the medication labels used in this study were of little or no use in improving patients' comprehension of the accompanying written instructions. PMID:15960849

Designing a strategy to promote safe, innovative off-label use of medications.

PubMed

Ansani, Nicole; Sirio, Carl; Smitherman, Thomas; Fedutes-Henderson, Bethany; Skledar, Susan; Weber, Robert J; Zgheib, Nathalie; Branch, Robert

2006-01-01

Innovative off-label medication use (defined as prescribing with reasonable rationale for use, but insufficient evidence to allay safety, efficacy, and cost-effectiveness concerns, yet is not clinical research) is common practice and provides challenges to ensuring high-quality health care and patient safety. This article describes a strategy to promote policy and standardization of innovative off-label medication use, ensure oversight of patient safety, and prospectively assess efficacy. A multidisciplinary group developed a policy and process to regulate innovative off-label medication use that standardizes formulary review, maximizes peer expertise input, and minimizes institution liability by evaluating the effectiveness of use, promoting evidence-based practices, and ensuring ethical obligations to patients and society. This strategy has been implemented through institutional staff structure. The review process balances benefits/risks for biologically plausible therapy that lacks rigorous data support. The authors' strategy illustrates collaboration that enables a priori consideration for innovative off-label medication use while providing safety surveillance and outcomes monitoring.

Label Design Affects Medication Safety in an Operating Room Crisis: A Controlled Simulation Study.

PubMed

Estock, Jamie L; Murray, Andrew W; Mizah, Margaret T; Mangione, Michael P; Goode, Joseph S; Eibling, David E

2018-06-01

Several factors contribute to medication errors in clinical practice settings, including the design of medication labels. The objective of this study was to quantify the impact of label design on medication safety in a realistic, high-stress clinical situation. Ninety-six anesthesia trainee participants were randomly assigned to either the redesigned or the current label condition. Participants were blinded to the study's focus on medication label design and their assigned label condition. Each participant was the sole anesthesia provider in a simulated operating room scenario involving an unexpected vascular injury. The surgeon asked the participant to administer hetastarch to the simulated patient because of hemodynamic instability. The fluid drawer of the anesthesia cart contained three 500-ml intravenous bags of hetastarch and one 500-ml intravenous bag of lidocaine. We hypothesized that redesigned labels would help participants correctly select hetastarch from the cart. If the participants incorrectly selected lidocaine from the cart, we hypothesized that the redesigned labels would help participants detect the lidocaine before administration. The percentage of participants who correctly selected hetastarch from the cart was significantly higher for the redesigned labels than the current labels (63% versus 40%; odds ratio, 2.61 [95% confidence interval, 1.1-6.1]; P = 0.03). Of the participants who incorrectly selected lidocaine from the cart, the percentage who detected the lidocaine before administration did not differ by label condition. The redesigned labels helped participants correctly select hetastarch from the cart, thus preventing some potentially catastrophic medication errors from reaching the simulated patient.

Convolutional Neural Networks for Medical Image Analysis: Full Training or Fine Tuning?

PubMed

Tajbakhsh, Nima; Shin, Jae Y; Gurudu, Suryakanth R; Hurst, R Todd; Kendall, Christopher B; Gotway, Michael B; Jianming Liang

2016-05-01

Training a deep convolutional neural network (CNN) from scratch is difficult because it requires a large amount of labeled training data and a great deal of expertise to ensure proper convergence. A promising alternative is to fine-tune a CNN that has been pre-trained using, for instance, a large set of labeled natural images. However, the substantial differences between natural and medical images may advise against such knowledge transfer. In this paper, we seek to answer the following central question in the context of medical image analysis: Can the use of pre-trained deep CNNs with sufficient fine-tuning eliminate the need for training a deep CNN from scratch? To address this question, we considered four distinct medical imaging applications in three specialties (radiology, cardiology, and gastroenterology) involving classification, detection, and segmentation from three different imaging modalities, and investigated how the performance of deep CNNs trained from scratch compared with the pre-trained CNNs fine-tuned in a layer-wise manner. Our experiments consistently demonstrated that 1) the use of a pre-trained CNN with adequate fine-tuning outperformed or, in the worst case, performed as well as a CNN trained from scratch; 2) fine-tuned CNNs were more robust to the size of training sets than CNNs trained from scratch; 3) neither shallow tuning nor deep tuning was the optimal choice for a particular application; and 4) our layer-wise fine-tuning scheme could offer a practical way to reach the best performance for the application at hand based on the amount of available data.

Evaluating Varied Label Designs for Use with Medical Devices: Optimized Labels Outperform Existing Labels in the Correct Selection of Devices and Time to Select.

PubMed

Bix, Laura; Seo, Do Chan; Ladoni, Moslem; Brunk, Eric; Becker, Mark W

2016-01-01

Effective standardization of medical device labels requires objective study of varied designs. Insufficient empirical evidence exists regarding how practitioners utilize and view labeling. Measure the effect of graphic elements (boxing information, grouping information, symbol use and color-coding) to optimize a label for comparison with those typical of commercial medical devices. Participants viewed 54 trials on a computer screen. Trials were comprised of two labels that were identical with regard to graphics, but differed in one aspect of information (e.g., one had latex, the other did not). Participants were instructed to select the label along a given criteria (e.g., latex containing) as quickly as possible. Dependent variables were binary (correct selection) and continuous (time to correct selection). Eighty-nine healthcare professionals were recruited at Association of Surgical Technologists (AST) conferences, and using a targeted e-mail of AST members. Symbol presence, color coding and grouping critical pieces of information all significantly improved selection rates and sped time to correct selection (α = 0.05). Conversely, when critical information was graphically boxed, probability of correct selection and time to selection were impaired (α = 0.05). Subsequently, responses from trials containing optimal treatments (color coded, critical information grouped with symbols) were compared to two labels created based on a review of those commercially available. Optimal labels yielded a significant positive benefit regarding the probability of correct choice ((P<0.0001) LSM; UCL, LCL: 97.3%; 98.4%, 95.5%)), as compared to the two labels we created based on commercial designs (92.0%; 94.7%, 87.9% and 89.8%; 93.0%, 85.3%) and time to selection. Our study provides data regarding design factors, namely: color coding, symbol use and grouping of critical information that can be used to significantly enhance the performance of medical device labels.

Constructing Benchmark Databases and Protocols for Medical Image Analysis: Diabetic Retinopathy

PubMed Central

Kauppi, Tomi; Kämäräinen, Joni-Kristian; Kalesnykiene, Valentina; Sorri, Iiris; Uusitalo, Hannu; Kälviäinen, Heikki

2013-01-01

We address the performance evaluation practices for developing medical image analysis methods, in particular, how to establish and share databases of medical images with verified ground truth and solid evaluation protocols. Such databases support the development of better algorithms, execution of profound method comparisons, and, consequently, technology transfer from research laboratories to clinical practice. For this purpose, we propose a framework consisting of reusable methods and tools for the laborious task of constructing a benchmark database. We provide a software tool for medical image annotation helping to collect class label, spatial span, and expert's confidence on lesions and a method to appropriately combine the manual segmentations from multiple experts. The tool and all necessary functionality for method evaluation are provided as public software packages. As a case study, we utilized the framework and tools to establish the DiaRetDB1 V2.1 database for benchmarking diabetic retinopathy detection algorithms. The database contains a set of retinal images, ground truth based on information from multiple experts, and a baseline algorithm for the detection of retinopathy lesions. PMID:23956787

Human induced pluripotent stem cells labeled with fluorescent magnetic nanoparticles for targeted imaging and hyperthermia therapy for gastric cancer.

PubMed

Li, Chao; Ruan, Jing; Yang, Meng; Pan, Fei; Gao, Guo; Qu, Su; Shen, You-Lan; Dang, Yong-Jun; Wang, Kan; Jin, Wei-Lin; Cui, Da-Xiang

2015-09-01

Human induced pluripotent stem (iPS) cells exhibit great potential for generating functional human cells for medical therapies. In this paper, we report for use of human iPS cells labeled with fluorescent magnetic nanoparticles (FMNPs) for targeted imaging and synergistic therapy of gastric cancer cells in vivo. Human iPS cells were prepared and cultured for 72 h. The culture medium was collected, and then was co-incubated with MGC803 cells. Cell viability was analyzed by the MTT method. FMNP-labeled human iPS cells were prepared and injected into gastric cancer-bearing nude mice. The mouse model was observed using a small-animal imaging system. The nude mice were irradiated under an external alternating magnetic field and evaluated using an infrared thermal mapping instrument. Tumor sizes were measured weekly. iPS cells and the collected culture medium inhibited the growth of MGC803 cells. FMNP-labeled human iPS cells targeted and imaged gastric cancer cells in vivo, as well as inhibited cancer growth in vivo through the external magnetic field. FMNP-labeled human iPS cells exhibit considerable potential in applications such as targeted dual-mode imaging and synergistic therapy for early gastric cancer.

Hierarchical Multi-atlas Label Fusion with Multi-scale Feature Representation and Label-specific Patch Partition

PubMed Central

Wu, Guorong; Kim, Minjeong; Sanroma, Gerard; Wang, Qian; Munsell, Brent C.; Shen, Dinggang

2014-01-01

Multi-atlas patch-based label fusion methods have been successfully used to improve segmentation accuracy in many important medical image analysis applications. In general, to achieve label fusion a single target image is first registered to several atlas images, after registration a label is assigned to each target point in the target image by determining the similarity between the underlying target image patch (centered at the target point) and the aligned image patch in each atlas image. To achieve the highest level of accuracy during the label fusion process it’s critical the chosen patch similarity measurement accurately captures the tissue/shape appearance of the anatomical structure. One major limitation of existing state-of-the-art label fusion methods is that they often apply a fixed size image patch throughout the entire label fusion procedure. Doing so may severely affect the fidelity of the patch similarity measurement, which in turn may not adequately capture complex tissue appearance patterns expressed by the anatomical structure. To address this limitation, we advance state-of-the-art by adding three new label fusion contributions: First, each image patch now characterized by a multi-scale feature representation that encodes both local and semi-local image information. Doing so will increase the accuracy of the patch-based similarity measurement. Second, to limit the possibility of the patch-based similarity measurement being wrongly guided by the presence of multiple anatomical structures in the same image patch, each atlas image patch is further partitioned into a set of label-specific partial image patches according to the existing labels. Since image information has now been semantically divided into different patterns, these new label-specific atlas patches make the label fusion process more specific and flexible. Lastly, in order to correct target points that are mislabeled during label fusion, a hierarchically approach is used to improve the

Cryo-imaging of fluorescently labeled single cells in a mouse

NASA Astrophysics Data System (ADS)

Steyer, Grant J.; Roy, Debashish; Salvado, Olivier; Stone, Meredith E.; Wilson, David L.

2009-02-01

We developed a cryo-imaging system to provide single-cell detection of fluorescently labeled cells in mouse, with particular applicability to stem cells and metastatic cancer. The Case cryoimaging system consists of a fluorescence microscope, robotic imaging positioner, customized cryostat, PC-based control system, and visualization/analysis software. The system alternates between sectioning (10-40 μm) and imaging, collecting color brightfield and fluorescent blockface image volumes >60GB. In mouse experiments, we imaged quantum-dot labeled stem cells, GFP-labeled cancer and stem cells, and cell-size fluorescent microspheres. To remove subsurface fluorescence, we used a simplified model of light-tissue interaction whereby the next image was scaled, blurred, and subtracted from the current image. We estimated scaling and blurring parameters by minimizing entropy of subtracted images. Tissue specific attenuation parameters were found [uT : heart (267 +/- 47.6 μm), liver (218 +/- 27.1 μm), brain (161 +/- 27.4 μm)] to be within the range of estimates in the literature. "Next image" processing removed subsurface fluorescence equally well across multiple tissues (brain, kidney, liver, adipose tissue, etc.), and analysis of 200 microsphere images in the brain gave 97+/-2% reduction of subsurface fluorescence. Fluorescent signals were determined to arise from single cells based upon geometric and integrated intensity measurements. Next image processing greatly improved axial resolution, enabled high quality 3D volume renderings, and improved enumeration of single cells with connected component analysis by up to 24%. Analysis of image volumes identified metastatic cancer sites, found homing of stem cells to injury sites, and showed microsphere distribution correlated with blood flow patterns. We developed and evaluated cryo-imaging to provide single-cell detection of fluorescently labeled cells in mouse. Our cryo-imaging system provides extreme (>60GB), micron

Medical Imaging System

NASA Technical Reports Server (NTRS)

1991-01-01

The MD Image System, a true-color image processing system that serves as a diagnostic aid and tool for storage and distribution of images, was developed by Medical Image Management Systems, Huntsville, AL, as a "spinoff from a spinoff." The original spinoff, Geostar 8800, developed by Crystal Image Technologies, Huntsville, incorporates advanced UNIX versions of ELAS (developed by NASA's Earth Resources Laboratory for analysis of Landsat images) for general purpose image processing. The MD Image System is an application of this technology to a medical system that aids in the diagnosis of cancer, and can accept, store and analyze images from other sources such as Magnetic Resonance Imaging.

18F-positron-emitting/fluorescent labeled erythrocytes allow imaging of internal hemorrhage in a murine intracranial hemorrhage model

PubMed Central

Wang, Ye; An, Fei-Fei; Chan, Mark; Friedman, Beth; Rodriguez, Erik A; Tsien, Roger Y; Aras, Omer

2017-01-01

An agent for visualizing cells by positron emission tomography is described and used to label red blood cells. The labeled red blood cells are injected systemically so that intracranial hemorrhage can be visualized by positron emission tomography (PET). Red blood cells are labeled with 0.3 µg of a positron-emitting, fluorescent multimodal imaging probe, and used to non-invasively image cryolesion induced intracranial hemorrhage in a murine model (BALB/c, 2.36 × 108 cells, 100 µCi, <4 mm hemorrhage). Intracranial hemorrhage is confirmed by histology, fluorescence, bright-field, and PET ex vivo imaging. The low required activity, minimal mass, and high resolution of this technique make this strategy an attractive alternative for imaging intracranial hemorrhage. PET is one solution to a spectrum of issues that complicate single photon emission computed tomography (SPECT). For this reason, this application serves as a PET alternative to [99mTc]-agents, and SPECT technology that is used in 2 million annual medical procedures. PET contrast is also superior to gadolinium and iodide contrast angiography for its lack of clinical contraindications. PMID:28054494

Film labels: a new look.

PubMed

Hunter, T B

1994-02-01

Every diagnostic image should be properly labeled. To improve the labeling of radiographs in the Department of Radiology at the University Medical Center, Tucson, Arizona, a special computer program was written to control the printing of the department's film flashcards. This program captures patient data from the hospital's radiology information system and uses it to create a film flashcard that contains the patient's name, hospital number, date of birth, age, the time the patient checked into the radiology department, and the date of the examination. The resulting film labels are legible and aesthetically pleasing. Having the patient's age and date of birth on the labels is a useful quality assurance measure to make certain the proper study has been performed on the correct patient. All diagnostic imaging departments should institute measures to assure their film labeling is as legible and informative as possible.

Segmentation of deformable organs from medical images using particle swarm optimization and nonlinear shape priors

NASA Astrophysics Data System (ADS)

Afifi, Ahmed; Nakaguchi, Toshiya; Tsumura, Norimichi

2010-03-01

In many medical applications, the automatic segmentation of deformable organs from medical images is indispensable and its accuracy is of a special interest. However, the automatic segmentation of these organs is a challenging task according to its complex shape. Moreover, the medical images usually have noise, clutter, or occlusion and considering the image information only often leads to meager image segmentation. In this paper, we propose a fully automated technique for the segmentation of deformable organs from medical images. In this technique, the segmentation is performed by fitting a nonlinear shape model with pre-segmented images. The kernel principle component analysis (KPCA) is utilized to capture the complex organs deformation and to construct the nonlinear shape model. The presegmentation is carried out by labeling each pixel according to its high level texture features extracted using the overcomplete wavelet packet decomposition. Furthermore, to guarantee an accurate fitting between the nonlinear model and the pre-segmented images, the particle swarm optimization (PSO) algorithm is employed to adapt the model parameters for the novel images. In this paper, we demonstrate the competence of proposed technique by implementing it to the liver segmentation from computed tomography (CT) scans of different patients.

Copper-64 Labeled Liposomes for Imaging Bone Marrow

PubMed Central

Lee, Sang-gyu; Gangangari, Kishore; Kalidindi, Teja Muralidhar; Punzalan, Blesida; Larson, Steven M.; Pillarsetty, Naga Vara Kishore

2016-01-01

Introduction Bone marrow is the soft tissue compartment inside the bones made up of hematopoietic cells, adipocytes, stromal cells, phagocytic cells, stem cells, and sinusoids. While [18F]-FLT has been utilized to image proliferative marrow, to date, there are no reports of particle based positron emission tomography (PET) imaging agents for imaging bone marrow. We have developed copper-64 labeled liposomal formulation that selectively targets bone marrow and therefore serves as an efficient PET probe for imaging bone marrow. Methods Optimized liposomal formulations were prepared with succinyl PE, DSPC, cholesterol, and mPEG-DSPE (69:39:1:10:0.1) with diameters of 90 and 140 nm, and were doped with DOTA-Bn-DSPE for stable 64Cu incorporation into liposomes. Results PET imaging and biodistribution studies with 64Cu-labeled liposomes indicate that accumulation in bone marrow was as high as 15.18 ± 3.69 %ID/g for 90 nm liposomes and 7.01 ± 0.92 %ID/g for 140 nm liposomes at 24 h post-administration. In vivo biodistribution studies in tumor-bearing mice indicate that the uptake of 90 nm particles is approximately 0.89 ± 0.48 %ID/g in tumor and 14.22 ± 8.07 %ID/g in bone marrow, but respective values for Doxil® like liposomes are 0.83 ± 0.49 %ID/g and 2.23 ± 1.00 %ID/g. Conclusion Our results indicate that our novel PET labeled liposomes target bone marrow with very high efficiency and therefore can function as efficient bone marrow imaging agents. PMID:27694056

Impact of brand or generic labeling on medication effectiveness and side effects.

PubMed

Faasse, Kate; Martin, Leslie R; Grey, Andrew; Gamble, Greg; Petrie, Keith J

2016-02-01

Branding medication with a known pharmaceutical company name or product name bestows on the drug an added assurance of authenticity and effectiveness compared to a generic preparation. This study examined the impact of brand name and generic labeling on medication effectiveness and side effects. 87 undergraduate students with frequent headaches took part in the study. Using a within-subjects counterbalanced design, each participant took tablets labeled either as brand name "Nurofen" or "Generic Ibuprofen" to treat each of 4 headaches. In reality, half of the tablets were placebos, and half were active ibuprofen (400 mg). Participants recorded their headache pain on a verbal descriptor and visual analogue scale prior to taking the tablets, and again 1 hour afterward. Medication side effects were also reported. Pain reduction following the use of brand name labeled tablets was similar in active ibuprofen or a placebo. However, if the tablets had a generic label, placebo tablets were significantly less effective compared to active ibuprofen. Fewer side effects were attributed to placebo tablets with brand name labeling compared to the same placebo tablets with a generic label. Branding of a tablet appears to have conferred a treatment benefit in the absence of an active ingredient, while generic labeled tablets were substantially less effective if they contained no active ingredient. Branding is also associated with reduced attribution of side effects to placebo tablets. Future interventions to improve perceptions of generics may have utility in improving treatment outcomes from generic drugs. (c) 2016 APA, all rights reserved).

Advances in medical image computing.

PubMed

Tolxdorff, T; Deserno, T M; Handels, H; Meinzer, H-P

2009-01-01

Medical image computing has become a key technology in high-tech applications in medicine and an ubiquitous part of modern imaging systems and the related processes of clinical diagnosis and intervention. Over the past years significant progress has been made in the field, both on methodological and on application level. Despite this progress there are still big challenges to meet in order to establish image processing routinely in health care. In this issue, selected contributions of the German Conference on Medical Image Processing (BVM) are assembled to present latest advances in the field of medical image computing. The winners of scientific awards of the German Conference on Medical Image Processing (BVM) 2008 were invited to submit a manuscript on their latest developments and results for possible publication in Methods of Information in Medicine. Finally, seven excellent papers were selected to describe important aspects of recent advances in the field of medical image processing. The selected papers give an impression of the breadth and heterogeneity of new developments. New methods for improved image segmentation, non-linear image registration and modeling of organs are presented together with applications of image analysis methods in different medical disciplines. Furthermore, state-of-the-art tools and techniques to support the development and evaluation of medical image processing systems in practice are described. The selected articles describe different aspects of the intense development in medical image computing. The image processing methods presented enable new insights into the patient's image data and have the future potential to improve medical diagnostics and patient treatment.

Specific in vivo labeling with GFP retroviruses, lentiviruses, and adenoviruses for imaging

NASA Astrophysics Data System (ADS)

Hoffman, Robert M.; Kishimoto, Hiroyuki; Fujiwara, Toshiyoshi

2008-02-01

Fluorescent proteins have revolutionized the field of imaging. Our laboratory pioneered in vivo imaging with fluorescent proteins. Fluorescent proteins have enabled imaging at the subcellular level in mice. We review here the use of different vectors carrying fluorescent proteins to selectively label normal and tumor tissue in vivo. We show that a GFP retrovirus and telomerase-driven GFP adenovirus can selectively label tumors in mice. We also show that a GFP lentivirus can selectively label the liver in mice. The practical application of these results are discussed.

A Locality-Constrained and Label Embedding Dictionary Learning Algorithm for Image Classification.

PubMed

Zhengming Li; Zhihui Lai; Yong Xu; Jian Yang; Zhang, David

2017-02-01

Locality and label information of training samples play an important role in image classification. However, previous dictionary learning algorithms do not take the locality and label information of atoms into account together in the learning process, and thus their performance is limited. In this paper, a discriminative dictionary learning algorithm, called the locality-constrained and label embedding dictionary learning (LCLE-DL) algorithm, was proposed for image classification. First, the locality information was preserved using the graph Laplacian matrix of the learned dictionary instead of the conventional one derived from the training samples. Then, the label embedding term was constructed using the label information of atoms instead of the classification error term, which contained discriminating information of the learned dictionary. The optimal coding coefficients derived by the locality-based and label-based reconstruction were effective for image classification. Experimental results demonstrated that the LCLE-DL algorithm can achieve better performance than some state-of-the-art algorithms.

Medical Imaging.

ERIC Educational Resources Information Center

Barker, M. C. J.

1996-01-01

Discusses four main types of medical imaging (x-ray, radionuclide, ultrasound, and magnetic resonance) and considers their relative merits. Describes important recent and possible future developments in image processing. (Author/MKR)

Medical Image Tamper Detection Based on Passive Image Authentication.

PubMed

Ulutas, Guzin; Ustubioglu, Arda; Ustubioglu, Beste; V Nabiyev, Vasif; Ulutas, Mustafa

2017-12-01

Telemedicine has gained popularity in recent years. Medical images can be transferred over the Internet to enable the telediagnosis between medical staffs and to make the patient's history accessible to medical staff from anywhere. Therefore, integrity protection of the medical image is a serious concern due to the broadcast nature of the Internet. Some watermarking techniques are proposed to control the integrity of medical images. However, they require embedding of extra information (watermark) into image before transmission. It decreases visual quality of the medical image and can cause false diagnosis. The proposed method uses passive image authentication mechanism to detect the tampered regions on medical images. Structural texture information is obtained from the medical image by using local binary pattern rotation invariant (LBPROT) to make the keypoint extraction techniques more successful. Keypoints on the texture image are obtained with scale invariant feature transform (SIFT). Tampered regions are detected by the method by matching the keypoints. The method improves the keypoint-based passive image authentication mechanism (they do not detect tampering when the smooth region is used for covering an object) by using LBPROT before keypoint extraction because smooth regions also have texture information. Experimental results show that the method detects tampered regions on the medical images even if the forged image has undergone some attacks (Gaussian blurring/additive white Gaussian noise) or the forged regions are scaled/rotated before pasting.

Automatic labeling of MR brain images through extensible learning and atlas forests.

PubMed

Xu, Lijun; Liu, Hong; Song, Enmin; Yan, Meng; Jin, Renchao; Hung, Chih-Cheng

2017-12-01

Multiatlas-based method is extensively used in MR brain images segmentation because of its simplicity and robustness. This method provides excellent accuracy although it is time consuming and limited in terms of obtaining information about new atlases. In this study, an automatic labeling of MR brain images through extensible learning and atlas forest is presented to address these limitations. We propose an extensible learning model which allows the multiatlas-based framework capable of managing the datasets with numerous atlases or dynamic atlas datasets and simultaneously ensure the accuracy of automatic labeling. Two new strategies are used to reduce the time and space complexity and improve the efficiency of the automatic labeling of brain MR images. First, atlases are encoded to atlas forests through random forest technology to reduce the time consumed for cross-registration between atlases and target image, and a scatter spatial vector is designed to eliminate errors caused by inaccurate registration. Second, an atlas selection method based on the extensible learning model is used to select atlases for target image without traversing the entire dataset and then obtain the accurate labeling. The labeling results of the proposed method were evaluated in three public datasets, namely, IBSR, LONI LPBA40, and ADNI. With the proposed method, the dice coefficient metric values on the three datasets were 84.17 ± 4.61%, 83.25 ± 4.29%, and 81.88 ± 4.53% which were 5% higher than those of the conventional method, respectively. The efficiency of the extensible learning model was evaluated by state-of-the-art methods for labeling of MR brain images. Experimental results showed that the proposed method could achieve accurate labeling for MR brain images without traversing the entire datasets. In the proposed multiatlas-based method, extensible learning and atlas forests were applied to control the automatic labeling of brain anatomies on large atlas datasets or dynamic

SIMulation of Medication Error induced by Clinical Trial drug labeling: the SIMME-CT study.

PubMed

Dollinger, Cecile; Schwiertz, Vérane; Sarfati, Laura; Gourc-Berthod, Chloé; Guédat, Marie-Gabrielle; Alloux, Céline; Vantard, Nicolas; Gauthier, Noémie; He, Sophie; Kiouris, Elena; Caffin, Anne-Gaelle; Bernard, Delphine; Ranchon, Florence; Rioufol, Catherine

2016-06-01

To assess the impact of investigational drug labels on the risk of medication error in drug dispensing. A simulation-based learning program focusing on investigational drug dispensing was conducted. The study was undertaken in an Investigational Drugs Dispensing Unit of a University Hospital of Lyon, France. Sixty-three pharmacy workers (pharmacists, residents, technicians or students) were enrolled. Ten risk factors were selected concerning label information or the risk of confusion with another clinical trial. Each risk factor was scored independently out of 5: the higher the score, the greater the risk of error. From 400 labels analyzed, two groups were selected for the dispensing simulation: 27 labels with high risk (score ≥3) and 27 with low risk (score ≤2). Each question in the learning program was displayed as a simulated clinical trial prescription. Medication error was defined as at least one erroneous answer (i.e. error in drug dispensing). For each question, response times were collected. High-risk investigational drug labels correlated with medication error and slower response time. Error rates were significantly 5.5-fold higher for high-risk series. Error frequency was not significantly affected by occupational category or experience in clinical trials. SIMME-CT is the first simulation-based learning tool to focus on investigational drug labels as a risk factor for medication error. SIMME-CT was also used as a training tool for staff involved in clinical research, to develop medication error risk awareness and to validate competence in continuing medical education. © The Author 2016. Published by Oxford University Press in association with the International Society for Quality in Health Care; all rights reserved.

Multi-atlas label fusion using hybrid of discriminative and generative classifiers for segmentation of cardiac MR images.

PubMed

Sedai, Suman; Garnavi, Rahil; Roy, Pallab; Xi Liang

2015-08-01

Multi-atlas segmentation first registers each atlas image to the target image and transfers the label of atlas image to the coordinate system of the target image. The transferred labels are then combined, using a label fusion algorithm. In this paper, we propose a novel label fusion method which aggregates discriminative learning and generative modeling for segmentation of cardiac MR images. First, a probabilistic Random Forest classifier is trained as a discriminative model to obtain the prior probability of a label at the given voxel of the target image. Then, a probability distribution of image patches is modeled using Gaussian Mixture Model for each label, providing the likelihood of the voxel belonging to the label. The final label posterior is obtained by combining the classification score and the likelihood score under Bayesian rule. Comparative study performed on MICCAI 2013 SATA Segmentation Challenge demonstrates that our proposed hybrid label fusion algorithm is accurate than other five state-of-the-art label fusion methods. The proposed method obtains dice similarity coefficient of 0.94 and 0.92 in segmenting epicardium and endocardium respectively. Moreover, our label fusion method achieves more accurate segmentation results compared to four other label fusion methods.

A coarse-to-fine approach for medical hyperspectral image classification with sparse representation

NASA Astrophysics Data System (ADS)

Chang, Lan; Zhang, Mengmeng; Li, Wei

2017-10-01

A coarse-to-fine approach with sparse representation is proposed for medical hyperspectral image classification in this work. Segmentation technique with different scales is employed to exploit edges of the input image, where coarse super-pixel patches provide global classification information while fine ones further provide detail information. Different from common RGB image, hyperspectral image has multi bands to adjust the cluster center with more high precision. After segmentation, each super pixel is classified by recently-developed sparse representation-based classification (SRC), which assigns label for testing samples in one local patch by means of sparse linear combination of all the training samples. Furthermore, segmentation with multiple scales is employed because single scale is not suitable for complicate distribution of medical hyperspectral imagery. Finally, classification results for different sizes of super pixel are fused by some fusion strategy, offering at least two benefits: (1) the final result is obviously superior to that of segmentation with single scale, and (2) the fusion process significantly simplifies the choice of scales. Experimental results using real medical hyperspectral images demonstrate that the proposed method outperforms the state-of-the-art SRC.

Intelligent distributed medical image management

NASA Astrophysics Data System (ADS)

Garcia, Hong-Mei C.; Yun, David Y.

1995-05-01

The rapid advancements in high performance global communication have accelerated cooperative image-based medical services to a new frontier. Traditional image-based medical services such as radiology and diagnostic consultation can now fully utilize multimedia technologies in order to provide novel services, including remote cooperative medical triage, distributed virtual simulation of operations, as well as cross-country collaborative medical research and training. Fast (efficient) and easy (flexible) retrieval of relevant images remains a critical requirement for the provision of remote medical services. This paper describes the database system requirements, identifies technological building blocks for meeting the requirements, and presents a system architecture for our target image database system, MISSION-DBS, which has been designed to fulfill the goals of Project MISSION (medical imaging support via satellite integrated optical network) -- an experimental high performance gigabit satellite communication network with access to remote supercomputing power, medical image databases, and 3D visualization capabilities in addition to medical expertise anywhere and anytime around the country. The MISSION-DBS design employs a synergistic fusion of techniques in distributed databases (DDB) and artificial intelligence (AI) for storing, migrating, accessing, and exploring images. The efficient storage and retrieval of voluminous image information is achieved by integrating DDB modeling and AI techniques for image processing while the flexible retrieval mechanisms are accomplished by combining attribute- based and content-based retrievals.

Labeling tetracysteine-tagged proteins with biarsenical dyes for live cell imaging.

PubMed

Gaietta, Guido M; Deerinck, Thomas J; Ellisman, Mark H

2011-01-01

Correlation of real-time or time-lapse light microscopy (LM) with electron microscopy (EM) of cells can be performed with biarsenical dyes. These dyes fluorescently label tetracysteine-tagged proteins so that they can be imaged with LM and, upon fluorescent photoconversion of 3,3'-diaminobenzidine tetrahydrochloride (DAB), with EM as well. In the following protocol, cells expressing tetracysteine-tagged proteins are labeled for 1 h with biarsenical dyes. The volumes indicated are for a single 30-mm culture dish containing 2 mL of labeling medium. Scale the suggested volumes up or down depending upon the size of the culture dish used in the labeling. The same procedure can be adapted for longer labeling times by lowering the amount of dye used to 50-100 nM; however, the amount of the competing dithiol EDT is maintained at 10-20 μM. Longer labeling times often produce higher signal-to-noise ratios and cause less trauma to the treated cells prior to imaging.

Label-free DNA imaging in vivo with stimulated Raman scattering microscopy

DOE PAGES

Lu, Fa-Ke; Basu, Srinjan; Igras, Vivien; ...

2015-08-31

Label-free DNA imaging is highly desirable in biology and medicine to perform live imaging without affecting cell function and to obtain instant histological tissue examination during surgical procedures. Here we show a label-free DNA imaging method with stimulated Raman scattering (SRS) microscopy for visualization of the cell nuclei in live animals and intact fresh human tissues with subcellular resolution. Relying on the distinct Raman spectral features of the carbon-hydrogen bonds in DNA, the distribution of DNA is retrieved from the strong background of proteins and lipids by linear decomposition of SRS images at three optimally selected Raman shifts. Based onmore » changes on DNA condensation in the nucleus, we were able to capture chromosome dynamics during cell division both in vitro and in vivo. We tracked mouse skin cell proliferation, induced by drug treatment, through in vivo counting of the mitotic rate. Moreover, we demonstrated a label-free histology method for human skin cancer diagnosis that provides comparable results to other conventional tissue staining methods such as H&E. In conclusion, our approach exhibits higher sensitivity than SRS imaging of DNA in the fingerprint spectral region. Compared with spontaneous Raman imaging of DNA, our approach is three orders of magnitude faster, allowing both chromatin dynamic studies and label-free optical histology in real time.« less

Label-free DNA imaging in vivo with stimulated Raman scattering microscopy

PubMed Central

Lu, Fa-Ke; Basu, Srinjan; Igras, Vivien; Hoang, Mai P.; Ji, Minbiao; Fu, Dan; Holtom, Gary R.; Neel, Victor A.; Freudiger, Christian W.; Fisher, David E.; Xie, X. Sunney

2015-01-01

Label-free DNA imaging is highly desirable in biology and medicine to perform live imaging without affecting cell function and to obtain instant histological tissue examination during surgical procedures. Here we show a label-free DNA imaging method with stimulated Raman scattering (SRS) microscopy for visualization of the cell nuclei in live animals and intact fresh human tissues with subcellular resolution. Relying on the distinct Raman spectral features of the carbon-hydrogen bonds in DNA, the distribution of DNA is retrieved from the strong background of proteins and lipids by linear decomposition of SRS images at three optimally selected Raman shifts. Based on changes on DNA condensation in the nucleus, we were able to capture chromosome dynamics during cell division both in vitro and in vivo. We tracked mouse skin cell proliferation, induced by drug treatment, through in vivo counting of the mitotic rate. Furthermore, we demonstrated a label-free histology method for human skin cancer diagnosis that provides comparable results to other conventional tissue staining methods such as H&E. Our approach exhibits higher sensitivity than SRS imaging of DNA in the fingerprint spectral region. Compared with spontaneous Raman imaging of DNA, our approach is three orders of magnitude faster, allowing both chromatin dynamic studies and label-free optical histology in real time. PMID:26324899

Clinical evaluation of watermarked medical images.

PubMed

Zain, Jasni M; Fauzi, Abdul M; Aziz, Azian A

2006-01-01

Digital watermarking medical images provides security to the images. The purpose of this study was to see whether digitally watermarked images changed clinical diagnoses when assessed by radiologists. We embedded 256 bits watermark to various medical images in the region of non-interest (RONI) and 480K bits in both region of interest (ROI) and RONI. Our results showed that watermarking medical images did not alter clinical diagnoses. In addition, there was no difference in image quality when visually assessed by the medical radiologists. We therefore concluded that digital watermarking medical images were safe in terms of preserving image quality for clinical purposes.

Medical Image Retrieval: A Multimodal Approach

PubMed Central

Cao, Yu; Steffey, Shawn; He, Jianbiao; Xiao, Degui; Tao, Cui; Chen, Ping; Müller, Henning

2014-01-01

Medical imaging is becoming a vital component of war on cancer. Tremendous amounts of medical image data are captured and recorded in a digital format during cancer care and cancer research. Facing such an unprecedented volume of image data with heterogeneous image modalities, it is necessary to develop effective and efficient content-based medical image retrieval systems for cancer clinical practice and research. While substantial progress has been made in different areas of content-based image retrieval (CBIR) research, direct applications of existing CBIR techniques to the medical images produced unsatisfactory results, because of the unique characteristics of medical images. In this paper, we develop a new multimodal medical image retrieval approach based on the recent advances in the statistical graphic model and deep learning. Specifically, we first investigate a new extended probabilistic Latent Semantic Analysis model to integrate the visual and textual information from medical images to bridge the semantic gap. We then develop a new deep Boltzmann machine-based multimodal learning model to learn the joint density model from multimodal information in order to derive the missing modality. Experimental results with large volume of real-world medical images have shown that our new approach is a promising solution for the next-generation medical imaging indexing and retrieval system. PMID:26309389

Site-Specific Bioorthogonal Labeling for Fluorescence Imaging of Intracellular Proteins in Living Cells.

PubMed

Peng, Tao; Hang, Howard C

2016-11-02

Over the past years, fluorescent proteins (e.g., green fluorescent proteins) have been widely utilized to visualize recombinant protein expression and localization in live cells. Although powerful, fluorescent protein tags are limited by their relatively large sizes and potential perturbation to protein function. Alternatively, site-specific labeling of proteins with small-molecule organic fluorophores using bioorthogonal chemistry may provide a more precise and less perturbing method. This approach involves site-specific incorporation of unnatural amino acids (UAAs) into proteins via genetic code expansion, followed by bioorthogonal chemical labeling with small organic fluorophores in living cells. While this approach has been used to label extracellular proteins for live cell imaging studies, site-specific bioorthogonal labeling and fluorescence imaging of intracellular proteins in live cells is still challenging. Herein, we systematically evaluate site-specific incorporation of diastereomerically pure bioorthogonal UAAs bearing stained alkynes or alkenes into intracellular proteins for inverse-electron-demand Diels-Alder cycloaddition reactions with tetrazine-functionalized fluorophores for live cell labeling and imaging in mammalian cells. Our studies show that site-specific incorporation of axial diastereomer of trans-cyclooct-2-ene-lysine robustly affords highly efficient and specific bioorthogonal labeling with monosubstituted tetrazine fluorophores in live mammalian cells, which enabled us to image the intracellular localization and real-time dynamic trafficking of IFITM3, a small membrane-associated protein with only 137 amino acids, for the first time. Our optimized UAA incorporation and bioorthogonal labeling conditions also enabled efficient site-specific fluorescence labeling of other intracellular proteins for live cell imaging studies in mammalian cells.

Rationale and design of a randomized trial to evaluate an evidence-based prescription drug label on actual medication use.

PubMed

Shrank, William H; Parker, Ruth; Davis, Terry; Pandit, Anjali U; Knox, Joann P; Moraras, Pear; Rademaker, Alfred; Wolf, Michael S

2010-11-01

Medication errors are an important public health concern, and poor understanding of medication labels are a root cause. Research shows that labels are variable, of poor quality, and not patient-centered. No real-world trials have evaluated whether improved medication labels can affect appropriate medication use, adherence or health outcomes. We developed an evidence-based prescription label that addresses both content and format. The enhanced label includes a universal medication schedule (UMS) that standardizes the directions for use incorporating 1) standard time periods for administration (morning, noon, evening, and bedtime), 2) numeric vs. alpha characters, 3) 'carriage returns' to separate daily dose and 4) a graphic aid to visually depict dose and frequency. We will evaluate the effect of providing this label to randomly sampled patients who receive their care from free clinics, mobile vans and federally qualified health centers (FQHCs) in Northern Virginia. We will recruit patients with diabetes or hypertension; these patients will be randomly assigned to receive all of their medications with improved labels or to receive prescriptions with standard labels. The primary outcome will be the patient's ability to correctly demonstrate dosing instructions. Other outcomes include adherence, error rates and health outcomes. To our knowledge, this trial is the first to evaluate the effect of prescription label improvement on understanding, medication use and outcomes in a clinical setting. If successful, these findings could be implemented broadly to promote safe and appropriate medication use and to support evidence-based standards in the development of labels. Copyright © 2010 Elsevier Inc. All rights reserved.

Imaging and Analytics: The changing face of Medical Imaging

NASA Astrophysics Data System (ADS)

Foo, Thomas

There have been significant technological advances in imaging capability over the past 40 years. Medical imaging capabilities have developed rapidly, along with technology development in computational processing speed and miniaturization. Moving to all-digital, the number of images that are acquired in a routine clinical examination has increased dramatically from under 50 images in the early days of CT and MRI to more than 500-1000 images today. The staggering number of images that are routinely acquired poses significant challenges for clinicians to interpret the data and to correctly identify the clinical problem. Although the time provided to render a clinical finding has not substantially changed, the amount of data available for interpretation has grown exponentially. In addition, the image quality (spatial resolution) and information content (physiologically-dependent image contrast) has also increased significantly with advances in medical imaging technology. On its current trajectory, medical imaging in the traditional sense is unsustainable. To assist in filtering and extracting the most relevant data elements from medical imaging, image analytics will have a much larger role. Automated image segmentation, generation of parametric image maps, and clinical decision support tools will be needed and developed apace to allow the clinician to manage, extract and utilize only the information that will help improve diagnostic accuracy and sensitivity. As medical imaging devices continue to improve in spatial resolution, functional and anatomical information content, image/data analytics will be more ubiquitous and integral to medical imaging capability.

Robust multi-atlas label propagation by deep sparse representation

PubMed Central

Zu, Chen; Wang, Zhengxia; Zhang, Daoqiang; Liang, Peipeng; Shi, Yonghong; Shen, Dinggang; Wu, Guorong

2016-01-01

Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of atlas image patches with similar anatomical structures. However, such assumption on image patch representation does not always hold in label fusion since (1) the image content within the patch may be corrupted due to noise and artifact; and (2) the distribution of morphometric patterns among atlas patches might be unbalanced such that the majority patterns can dominate label fusion result over other minority patterns. The violation of the above basic assumptions could significantly undermine the label fusion accuracy. To overcome these issues, we first consider forming label-specific group for the atlas patches with the same label. Then, we alter the conventional flat and shallow dictionary to a deep multi-layer structure, where the top layer (label-specific dictionaries) consists of groups of representative atlas patches and the subsequent layers (residual dictionaries) hierarchically encode the patchwise residual information in different scales. Thus, the label fusion follows the representation consensus across representative dictionaries. However, the representation of target patch in each group is iteratively optimized by using the representative atlas patches in each label-specific dictionary exclusively to match the principal patterns and also using all residual patterns across groups collaboratively to overcome the issue that some groups might be absent of certain variation patterns presented in the target image patch. Promising segmentation results have been achieved in labeling hippocampus on ADNI dataset, as well as basal ganglia and brainstem structures, compared

Robust multi-atlas label propagation by deep sparse representation.

PubMed

Zu, Chen; Wang, Zhengxia; Zhang, Daoqiang; Liang, Peipeng; Shi, Yonghong; Shen, Dinggang; Wu, Guorong

2017-03-01

Recently, multi-atlas patch-based label fusion has achieved many successes in medical imaging area. The basic assumption in the current state-of-the-art approaches is that the image patch at the target image point can be represented by a patch dictionary consisting of atlas patches from registered atlas images. Therefore, the label at the target image point can be determined by fusing labels of atlas image patches with similar anatomical structures. However, such assumption on image patch representation does not always hold in label fusion since (1) the image content within the patch may be corrupted due to noise and artifact; and (2) the distribution of morphometric patterns among atlas patches might be unbalanced such that the majority patterns can dominate label fusion result over other minority patterns. The violation of the above basic assumptions could significantly undermine the label fusion accuracy. To overcome these issues, we first consider forming label-specific group for the atlas patches with the same label. Then, we alter the conventional flat and shallow dictionary to a deep multi-layer structure, where the top layer ( label-specific dictionaries ) consists of groups of representative atlas patches and the subsequent layers ( residual dictionaries ) hierarchically encode the patchwise residual information in different scales. Thus, the label fusion follows the representation consensus across representative dictionaries. However, the representation of target patch in each group is iteratively optimized by using the representative atlas patches in each label-specific dictionary exclusively to match the principal patterns and also using all residual patterns across groups collaboratively to overcome the issue that some groups might be absent of certain variation patterns presented in the target image patch. Promising segmentation results have been achieved in labeling hippocampus on ADNI dataset, as well as basal ganglia and brainstem structures

Learning a Dictionary of Shape Epitomes with Applications to Image Labeling

PubMed Central

Chen, Liang-Chieh; Papandreou, George; Yuille, Alan L.

2015-01-01

The first main contribution of this paper is a novel method for representing images based on a dictionary of shape epitomes. These shape epitomes represent the local edge structure of the image and include hidden variables to encode shift and rotations. They are learnt in an unsupervised manner from groundtruth edges. This dictionary is compact but is also able to capture the typical shapes of edges in natural images. In this paper, we illustrate the shape epitomes by applying them to the image labeling task. In other work, described in the supplementary material, we apply them to edge detection and image modeling. We apply shape epitomes to image labeling by using Conditional Random Field (CRF) Models. They are alternatives to the superpixel or pixel representations used in most CRFs. In our approach, the shape of an image patch is encoded by a shape epitome from the dictionary. Unlike the superpixel representation, our method avoids making early decisions which cannot be reversed. Our resulting hierarchical CRFs efficiently capture both local and global class co-occurrence properties. We demonstrate its quantitative and qualitative properties of our approach with image labeling experiments on two standard datasets: MSRC-21 and Stanford Background. PMID:26321886

Parental misinterpretations of over-the-counter pediatric cough and cold medication labels.

PubMed

Lokker, Nicole; Sanders, Lee; Perrin, Eliana M; Kumar, Disha; Finkle, Joanne; Franco, Vivian; Choi, Leena; Johnston, Philip E; Rothman, Russell L

2009-06-01

Concerns about the safety and efficacy of over-the-counter cold medications have led to a recent US Food and Drug Administration public health advisory against their use in children <2 years of age. Our goal was to examine caregiver understanding of the age indication of over-the-counter cold medication labels and identify factors, associated with caregiver understanding. Caregivers of infant children (< or =1 year old) were recruited from clinics at 3 institutions. Questions were administered regarding the use of 4 previously common "infant" over-the-counter cold and cough medicines labeled to consult a physician if used in children <2 years of age. Literacy and numeracy skills were assessed with validated instruments. A total of 182 caregivers were recruited; 87% were the infants' mothers. Mean education level was 12.5 years, and 99% had adequate literacy skills, but only 17% had >9th-grade numeracy skills. When examining the front of the product label, 86% of the time parents thought these products were appropriate for use in children <2 years of age. More than 50% of the time, parents stated they would give these over-the-counter products to a 13-month-old child with cold symptoms. Common factors that influenced parental decisions included label saying "infant," graphics (eg, infants, teddy bears, droppers), and dosing directions. Caregivers were influenced by the dosing directions only 47% of the time. Caregivers with lower numeracy skills were more likely to provide inappropriate reasons for giving an over-the-counter medication. Misunderstanding of over-the-counter cold products is common and could result in harm if medications are given inappropriately. Label language and graphics seem to influence inappropriate interpretation of over-the-counter product age indications. Poorer parental numeracy skills may increase the misinterpretation of these products. Opportunities exist for the Food and Drug Administration and manufacturers to revise existing labels to

Labeling of macrophages using bacterial magnetosomes and their characterization by magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Hartung, Annegret; Lisy, Marcus R.; Herrmann, Karl-Heinz; Hilger, Ingrid; Schüler, Dirk; Lang, Claus; Bellemann, Matthias E.; Kaiser, Werner A.; Reichenbach, Jürgen R.

2007-04-01

This work investigated macrophages labeled with magnetosomes for the possible detection of inflammations by MR molecular imaging. Pure magnetosomes and macrophages containing magnetosomes were analyzed using a clinical 1.5 T MR-scanner. Relaxivities of magnetosomes and relaxation rates of cells containing magnetosomes were determined. Peritonitis was induced in two mice. T1, T2 and T2* weighted images were acquired following injection of the probes. Pure magnetosomes and labeled cells showed slight effects on T1, but strong effects on T2 and T2* images. Labeled macrophages were located with magnetic resonance imaging (MRI) in the colon area, thus demonstrating the feasibility of the proposed approach.

Collaborative labeling of malignant glioma with WebMILL: a first look

NASA Astrophysics Data System (ADS)

Singh, Eesha; Asman, Andrew J.; Xu, Zhoubing; Chambless, Lola; Thompson, Reid; Landman, Bennett A.

2012-02-01

Malignant gliomas are the most common form of primary neoplasm in the central nervous system, and one of the most rapidly fatal of all human malignancies. They are treated by maximal surgical resection followed by radiation and chemotherapy. Herein, we seek to improve the methods available to quantify the extent of tumors using newly presented, collaborative labeling techniques on magnetic resonance imaging. Traditionally, labeling medical images has entailed that expert raters operate on one image at a time, which is resource intensive and not practical for very large datasets. Using many, minimally trained raters to label images has the possibility of minimizing laboratory requirements and allowing high degrees of parallelism. A successful effort also has the possibility of reducing overall cost. This potentially transformative technology presents a new set of problems, because one must pose the labeling challenge in a manner accessible to people with little or no background in labeling medical images and raters cannot be expected to read detailed instructions. Hence, a different training method has to be employed. The training must appeal to all types of learners and have the same concepts presented in multiple ways to ensure that all the subjects understand the basics of labeling. Our overall objective is to demonstrate the feasibility of studying malignant glioma morphometry through statistical analysis of the collaborative efforts of many, minimally-trained raters. This study presents preliminary results on optimization of the WebMILL framework for neoplasm labeling and investigates the initial contributions of 78 raters labeling 98 whole-brain datasets.

Chelator-Free Labeling of Layered Double Hydroxide Nanoparticles for in Vivo PET Imaging

NASA Astrophysics Data System (ADS)

Shi, Sixiang; Fliss, Brianne C.; Gu, Zi; Zhu, Yian; Hong, Hao; Valdovinos, Hector F.; Hernandez, Reinier; Goel, Shreya; Luo, Haiming; Chen, Feng; Barnhart, Todd E.; Nickles, Robert J.; Xu, Zhi Ping; Cai, Weibo

2015-11-01

Layered double hydroxide (LDH) nanomaterial has emerged as a novel delivery agent for biomedical applications due to its unique structure and properties. However, in vivo positron emission tomography (PET) imaging with LDH nanoparticles has not been achieved. The aim of this study is to explore chelator-free labeling of LDH nanoparticles with radioisotopes for in vivo PET imaging. Bivalent cation 64Cu2+ and trivalent cation 44Sc3+ were found to readily label LDH nanoparticles with excellent labeling efficiency and stability, whereas tetravalent cation 89Zr4+ could not label LDH since it does not fit into the LDH crystal structure. PET imaging shows that prominent tumor uptake was achieved in 4T1 breast cancer with 64Cu-LDH-BSA via passive targeting alone (7.7 ± 0.1%ID/g at 16 h post-injection; n = 3). These results support that LDH is a versatile platform that can be labeled with various bivalent and trivalent radiometals without comprising the native properties, highly desirable for PET image-guided drug delivery.

Prioritization of brain MRI volumes using medical image perception model and tumor region segmentation.

PubMed

Mehmood, Irfan; Ejaz, Naveed; Sajjad, Muhammad; Baik, Sung Wook

2013-10-01

The objective of the present study is to explore prioritization methods in diagnostic imaging modalities to automatically determine the contents of medical images. In this paper, we propose an efficient prioritization of brain MRI. First, the visual perception of the radiologists is adapted to identify salient regions. Then this saliency information is used as an automatic label for accurate segmentation of brain lesion to determine the scientific value of that image. The qualitative and quantitative results prove that the rankings generated by the proposed method are closer to the rankings created by radiologists. Copyright © 2013 Elsevier Ltd. All rights reserved.

Tc-99m Labeled carrier for imaging

DOEpatents

Henze, Eberhard

1984-01-01

Novel radionuclide imaging agents, having particular application for lymphangiography are provided by non-covalently binding Tc-99m to a pharmaceutically acceptable cross-linked polysaccharide. Upon injection of the Tc-99m labeled polysaccharide into the blood stream, optimum contrast can be obtained within one hour.

A New F-18 Labeled PET Agent For Imaging Alzheimer's Plaques

NASA Astrophysics Data System (ADS)

Kulkarni, Padmakar V.; Vasdev, Neil; Hao, Guiyang; Arora, Veera; Long, Michael; Slavine, Nikolai; Chiguru, Srinivas; Qu, Bao Xi; Sun, Xiankai; Bennett, Michael; Antich, Peter P.; Bonte, Frederick J.

2011-06-01

Amyloid plaques and neurofibrillary tangles are hallmarks of Alzheimer's disease (AD). Advances in development of imaging agents have focused on targeting amyloid plaques. Notable success has been the development of C-11 labeled PIB (Pittsburgh Compound) and a number of studies have demonstrated the utility of this agent. However, the short half life of C-11 (t1/2: 20 min), is a limitation, thus has prompted the development of F-18 labeled agents. Most of these agents are derivatives of amyloid binding dyes; Congo red and Thioflavin. Some of these agents are in clinical trials with encouraging results. We have been exploring new class of agents based on 8-hydroxy quinoline, a weak metal chelator, targeting elevated levels of metals in plaques. Iodine-123 labeled clioquinol showed affinity for amyloid plaques however, it had limited brain uptake and was not successful in imaging in intact animals and humans. We have been successful in synthesizing F-18 labeled 8-hydroxy quinoline. Small animal PET/CT imaging studies with this agent showed high (7-10% ID/g), rapid brain uptake and fast washout of the agent from normal mice brains and delayed washout from transgenic Alzheimer's mice. These promising results encouraged us in further evaluation of this class of compounds for imaging AD plaques.

Intravital imaging by simultaneous label-free autofluorescence-multiharmonic microscopy.

PubMed

You, Sixian; Tu, Haohua; Chaney, Eric J; Sun, Yi; Zhao, Youbo; Bower, Andrew J; Liu, Yuan-Zhi; Marjanovic, Marina; Sinha, Saurabh; Pu, Yang; Boppart, Stephen A

2018-05-29

Intravital microscopy (IVM) emerged and matured as a powerful tool for elucidating pathways in biological processes. Although label-free multiphoton IVM is attractive for its non-perturbative nature, its wide application has been hindered, mostly due to the limited contrast of each imaging modality and the challenge to integrate them. Here we introduce simultaneous label-free autofluorescence-multiharmonic (SLAM) microscopy, a single-excitation source nonlinear imaging platform that uses a custom-designed excitation window at 1110 nm and shaped ultrafast pulses at 10 MHz to enable fast (2-orders-of-magnitude improvement), simultaneous, and efficient acquisition of autofluorescence (FAD and NADH) and second/third harmonic generation from a wide array of cellular and extracellular components (e.g., tumor cells, immune cells, vesicles, and vessels) in living tissue using only 14 mW for extended time-lapse investigations. Our work demonstrates the versatility and efficiency of SLAM microscopy for tracking cellular events in vivo, and is a major enabling advance in label-free IVM.

A transversal approach for patch-based label fusion via matrix completion

PubMed Central

Sanroma, Gerard; Wu, Guorong; Gao, Yaozong; Thung, Kim-Han; Guo, Yanrong; Shen, Dinggang

2015-01-01

Recently, multi-atlas patch-based label fusion has received an increasing interest in the medical image segmentation field. After warping the anatomical labels from the atlas images to the target image by registration, label fusion is the key step to determine the latent label for each target image point. Two popular types of patch-based label fusion approaches are (1) reconstruction-based approaches that compute the target labels as a weighted average of atlas labels, where the weights are derived by reconstructing the target image patch using the atlas image patches; and (2) classification-based approaches that determine the target label as a mapping of the target image patch, where the mapping function is often learned using the atlas image patches and their corresponding labels. Both approaches have their advantages and limitations. In this paper, we propose a novel patch-based label fusion method to combine the above two types of approaches via matrix completion (and hence, we call it transversal). As we will show, our method overcomes the individual limitations of both reconstruction-based and classification-based approaches. Since the labeling confidences may vary across the target image points, we further propose a sequential labeling framework that first labels the highly confident points and then gradually labels more challenging points in an iterative manner, guided by the label information determined in the previous iterations. We demonstrate the performance of our novel label fusion method in segmenting the hippocampus in the ADNI dataset, subcortical and limbic structures in the LONI dataset, and mid-brain structures in the SATA dataset. We achieve more accurate segmentation results than both reconstruction-based and classification-based approaches. Our label fusion method is also ranked 1st in the online SATA Multi-Atlas Segmentation Challenge. PMID:26160394

Content standards for medical image metadata

NASA Astrophysics Data System (ADS)

d'Ornellas, Marcos C.; da Rocha, Rafael P.

2003-12-01

Medical images are at the heart of the healthcare diagnostic procedures. They have provided not only a noninvasive mean to view anatomical cross-sections of internal organs but also a mean for physicians to evaluate the patient"s diagnosis and monitor the effects of the treatment. For a Medical Center, the emphasis may shift from the generation of image to post processing and data management since the medical staff may generate even more processed images and other data from the original image after various analyses and post processing. A medical image data repository for health care information system is becoming a critical need. This data repository would contain comprehensive patient records, including information such as clinical data and related diagnostic images, and post-processed images. Due to the large volume and complexity of the data as well as the diversified user access requirements, the implementation of the medical image archive system will be a complex and challenging task. This paper discusses content standards for medical image metadata. In addition it also focuses on the image metadata content evaluation and metadata quality management.

Readability of prescription labels and medication recall in a population of tertiary referral glaucoma patients.

PubMed

O'Hare, Fleur; Jeganathan, V Swetha E; Rokahr, Catherine G; Rogers, Sophie L; Crowston, Jonathan G

2009-12-01

To evaluate readability of eye drop labels and accurate recall of prescription instructions in a glaucoma population. A hospital-based, cross-sectional study. A trained, interviewer examined patient ability to read standard and larger font medication labels. A questionnaire was administered to ascertain accurate recall of prescribed eye drops. Clinical information was obtained through independent chart review. Glaucoma severity was classified according to a glaucoma staging system. The setting for the study was the glaucoma outpatient clinic, Royal Victorian Eye and Ear Hospital (Melbourne, Australia), a major tertiary referral centre. A total of 200 glaucoma patients (96.2% response), aged 45-90 years, on eye drops took part in the study. Non-English-speaking patients were excluded. The main outcome measure was the ability to read prescribed medication labels and accurately recall treatment regime was compared with glaucoma severity and the number of eye drops. Of the glaucoma patients, 12% were unable to read standard pharmacy labels. Only 5.5% were unable to read the larger font labels. Of the patients, 32% were not able to accurately recall the type of drops or prescribed frequency of instillation. An inability to read standard labels was associated with a threefold reduction in the likelihood of accurate medication recall (95% confidence intervals, 1.40-7.66, P < 0.05). Patients with three or more types of eye drops were five times less likely to recall their medications (95% confidence interval, 0.07-0.57, P < 0.05). Inability to read or recall prescribed eye drops was associated with glaucoma severity and the number of prescribed eye drops. These factors may impact significantly on patients' adherence to glaucoma medications.

Content-Based Medical Image Retrieval

NASA Astrophysics Data System (ADS)

Müller, Henning; Deserno, Thomas M.

This chapter details the necessity for alternative access concepts to the currently mainly text-based methods in medical information retrieval. This need is partly due to the large amount of visual data produced, the increasing variety of medical imaging data and changing user patterns. The stored visual data contain large amounts of unused information that, if well exploited, can help diagnosis, teaching and research. The chapter briefly reviews the history of image retrieval and its general methods before technologies that have been developed in the medical domain are focussed. We also discuss evaluation of medical content-based image retrieval (CBIR) systems and conclude with pointing out their strengths, gaps, and further developments. As examples, the MedGIFT project and the Image Retrieval in Medical Applications (IRMA) framework are presented.

21 CFR 892.2030 - Medical image digitizer.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Medical image digitizer. 892.2030 Section 892.2030...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2030 Medical image digitizer. (a) Identification. A medical image digitizer is a device intended to convert an analog medical image into a digital...

21 CFR 892.2030 - Medical image digitizer.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Medical image digitizer. 892.2030 Section 892.2030...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2030 Medical image digitizer. (a) Identification. A medical image digitizer is a device intended to convert an analog medical image into a digital...

21 CFR 892.2030 - Medical image digitizer.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Medical image digitizer. 892.2030 Section 892.2030...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2030 Medical image digitizer. (a) Identification. A medical image digitizer is a device intended to convert an analog medical image into a digital...

21 CFR 892.2030 - Medical image digitizer.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Medical image digitizer. 892.2030 Section 892.2030...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2030 Medical image digitizer. (a) Identification. A medical image digitizer is a device intended to convert an analog medical image into a digital...

Patient-reported outcomes to support medical product labeling claims: FDA perspective.

PubMed

Patrick, Donald L; Burke, Laurie B; Powers, John H; Scott, Jane A; Rock, Edwin P; Dawisha, Sahar; O'Neill, Robert; Kennedy, Dianne L

2007-01-01

This article concerns development and use of patient-reported outcomes (PROs) in clinical trials to evaluate medical products. A PRO is any report coming directly from patients, without interpretation by physicians or others, about how they function or feel in relation to a health condition and its therapy. PRO instruments are used to measure these patient reports. PROs provide a unique perspective on medical therapy, because some effects of a health condition and its therapy are known only to patients. Properly developed and evaluated PRO instruments also have the potential to provide more sensitive and specific measurements of the effects of medical therapies, thereby increasing the efficiency of clinical trials that attempt to measure the meaningful treatment benefits of those therapies. Poorly developed and evaluated instruments may provide misleading conclusions or data that cannot be used to support product labeling claims. We review selected major challenges from Food and Drug Administration's perspective in using PRO instruments, measures, and end points to support treatment benefit claims in product labeling. These challenges highlight the need for sponsors to formulate desired labeling claim(s) prospectively, to acquire and document information needed to support these claim(s), and to identify existing instruments or develop new and more appropriate PRO instruments for evaluating treatment benefit in the defined population in which they will seek claims.

Medical imaging: examples of clinical applications

NASA Astrophysics Data System (ADS)

Meinzer, H. P.; Thorn, M.; Vetter, M.; Hassenpflug, P.; Hastenteufel, M.; Wolf, I.

Clinical routine is currently producing a multitude of diagnostic digital images but only a few are used in therapy planning and treatment. Medical imaging is involved in both diagnosis and therapy. Using a computer, existing 2D images can be transformed into interactive 3D volumes and results from different modalities can be merged. Furthermore, it is possible to calculate functional areas that were not visible in the primary images. This paper presents examples of clinical applications that are integrated into clinical routine and are based on medical imaging fundamentals. In liver surgery, the importance of virtual planning is increasing because surgery is still the only possible curative procedure. Visualisation and analysis of heart defects are also gaining in significance due to improved surgery techniques. Finally, an outlook is provided on future developments in medical imaging using navigation to support the surgeon's work. The paper intends to give an impression of the wide range of medical imaging that goes beyond the mere calculation of medical images.

Self-assessed performance improves statistical fusion of image labels

PubMed Central

Bryan, Frederick W.; Xu, Zhoubing; Asman, Andrew J.; Allen, Wade M.; Reich, Daniel S.; Landman, Bennett A.

2014-01-01

Purpose: Expert manual labeling is the gold standard for image segmentation, but this process is difficult, time-consuming, and prone to inter-individual differences. While fully automated methods have successfully targeted many anatomies, automated methods have not yet been developed for numerous essential structures (e.g., the internal structure of the spinal cord as seen on magnetic resonance imaging). Collaborative labeling is a new paradigm that offers a robust alternative that may realize both the throughput of automation and the guidance of experts. Yet, distributing manual labeling expertise across individuals and sites introduces potential human factors concerns (e.g., training, software usability) and statistical considerations (e.g., fusion of information, assessment of confidence, bias) that must be further explored. During the labeling process, it is simple to ask raters to self-assess the confidence of their labels, but this is rarely done and has not been previously quantitatively studied. Herein, the authors explore the utility of self-assessment in relation to automated assessment of rater performance in the context of statistical fusion. Methods: The authors conducted a study of 66 volumes manually labeled by 75 minimally trained human raters recruited from the university undergraduate population. Raters were given 15 min of training during which they were shown examples of correct segmentation, and the online segmentation tool was demonstrated. The volumes were labeled 2D slice-wise, and the slices were unordered. A self-assessed quality metric was produced by raters for each slice by marking a confidence bar superimposed on the slice. Volumes produced by both voting and statistical fusion algorithms were compared against a set of expert segmentations of the same volumes. Results: Labels for 8825 distinct slices were obtained. Simple majority voting resulted in statistically poorer performance than voting weighted by self-assessed performance

Progressive Label Fusion Framework for Multi-atlas Segmentation by Dictionary Evolution

PubMed Central

Song, Yantao; Wu, Guorong; Sun, Quansen; Bahrami, Khosro; Li, Chunming; Shen, Dinggang

2015-01-01

Accurate segmentation of anatomical structures in medical images is very important in neuroscience studies. Recently, multi-atlas patch-based label fusion methods have achieved many successes, which generally represent each target patch from an atlas patch dictionary in the image domain and then predict the latent label by directly applying the estimated representation coefficients in the label domain. However, due to the large gap between these two domains, the estimated representation coefficients in the image domain may not stay optimal for the label fusion. To overcome this dilemma, we propose a novel label fusion framework to make the weighting coefficients eventually to be optimal for the label fusion by progressively constructing a dynamic dictionary in a layer-by-layer manner, where a sequence of intermediate patch dictionaries gradually encode the transition from the patch representation coefficients in image domain to the optimal weights for label fusion. Our proposed framework is general to augment the label fusion performance of the current state-of-the-art methods. In our experiments, we apply our proposed method to hippocampus segmentation on ADNI dataset and achieve more accurate labeling results, compared to the counterpart methods with single-layer dictionary. PMID:26942233

Progressive Label Fusion Framework for Multi-atlas Segmentation by Dictionary Evolution.

PubMed

Song, Yantao; Wu, Guorong; Sun, Quansen; Bahrami, Khosro; Li, Chunming; Shen, Dinggang

2015-10-01

Accurate segmentation of anatomical structures in medical images is very important in neuroscience studies. Recently, multi-atlas patch-based label fusion methods have achieved many successes, which generally represent each target patch from an atlas patch dictionary in the image domain and then predict the latent label by directly applying the estimated representation coefficients in the label domain. However, due to the large gap between these two domains, the estimated representation coefficients in the image domain may not stay optimal for the label fusion. To overcome this dilemma, we propose a novel label fusion framework to make the weighting coefficients eventually to be optimal for the label fusion by progressively constructing a dynamic dictionary in a layer-by-layer manner, where a sequence of intermediate patch dictionaries gradually encode the transition from the patch representation coefficients in image domain to the optimal weights for label fusion. Our proposed framework is general to augment the label fusion performance of the current state-of-the-art methods. In our experiments, we apply our proposed method to hippocampus segmentation on ADNI dataset and achieve more accurate labeling results, compared to the counterpart methods with single-layer dictionary.

77 FR 38177 - TRICARE; Off-Label Uses of Devices; Partial List of Examples of Unproven Drugs, Devices, Medical...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-27

... medical literature, national organizations, or technology assessment bodies that the off-label use is safe... medical literature, national organizations, or technology assessment bodies that the off-label use is safe.... Due to the rapid and extensive changes in medical technology it is not feasible to maintain this list...

Object-Location-Aware Hashing for Multi-Label Image Retrieval via Automatic Mask Learning.

PubMed

Huang, Chang-Qin; Yang, Shang-Ming; Pan, Yan; Lai, Han-Jiang

2018-09-01

Learning-based hashing is a leading approach of approximate nearest neighbor search for large-scale image retrieval. In this paper, we develop a deep supervised hashing method for multi-label image retrieval, in which we propose to learn a binary "mask" map that can identify the approximate locations of objects in an image, so that we use this binary "mask" map to obtain length-limited hash codes which mainly focus on an image's objects but ignore the background. The proposed deep architecture consists of four parts: 1) a convolutional sub-network to generate effective image features; 2) a binary "mask" sub-network to identify image objects' approximate locations; 3) a weighted average pooling operation based on the binary "mask" to obtain feature representations and hash codes that pay most attention to foreground objects but ignore the background; and 4) the combination of a triplet ranking loss designed to preserve relative similarities among images and a cross entropy loss defined on image labels. We conduct comprehensive evaluations on four multi-label image data sets. The results indicate that the proposed hashing method achieves superior performance gains over the state-of-the-art supervised or unsupervised hashing baselines.

In vivo fluorescence imaging of hepatocellular carcinoma xenograft using near-infrared labeled epidermal growth factor receptor (EGFR) peptide

PubMed Central

Li, Z.; Zhou, Q.; Zhou, J.; Duan, X.; Zhu, J.; Wang, T. D.

2016-01-01

Minimally-invasive surgery of hepatocellular carcinoma (HCC) can be limited by poor tumor visualization with white light. We demonstrate systemic administration of a Cy5.5-labeled peptide specific for epidermal growth factor receptor (EGFR) to target HCC in vivo in a mouse xenograft model. We attached a compact imaging module to the proximal end of a medical laparoscope to collect near-infrared fluorescence and reflectance images concurrently at 15 frames/sec. We measured a mean target-to-background ratio of 2.99 ± 0.22 from 13 surgically exposed subcutaneous human HCC tumors in vivo in 5 mice. This integrated imaging methodology is promising to guide laparoscopic resection of HCC. PMID:27699089

3D GRASE PROPELLER: improved image acquisition technique for arterial spin labeling perfusion imaging.

PubMed

Tan, Huan; Hoge, W Scott; Hamilton, Craig A; Günther, Matthias; Kraft, Robert A

2011-07-01

Arterial spin labeling is a noninvasive technique that can quantitatively measure cerebral blood flow. While traditionally arterial spin labeling employs 2D echo planar imaging or spiral acquisition trajectories, single-shot 3D gradient echo and spin echo (GRASE) is gaining popularity in arterial spin labeling due to inherent signal-to-noise ratio advantage and spatial coverage. However, a major limitation of 3D GRASE is through-plane blurring caused by T(2) decay. A novel technique combining 3D GRASE and a periodically rotated overlapping parallel lines with enhanced reconstruction trajectory (PROPELLER) is presented to minimize through-plane blurring without sacrificing perfusion sensitivity or increasing total scan time. Full brain perfusion images were acquired at a 3 × 3 × 5 mm(3) nominal voxel size with pulsed arterial spin labeling preparation sequence. Data from five healthy subjects was acquired on a GE 1.5T scanner in less than 4 minutes per subject. While showing good agreement in cerebral blood flow quantification with 3D gradient echo and spin echo, 3D GRASE PROPELLER demonstrated reduced through-plane blurring, improved anatomical details, high repeatability and robustness against motion, making it suitable for routine clinical use. Copyright © 2011 Wiley-Liss, Inc.

Off-label use of medical products in radiation therapy: Summary of the Report of AAPM Task Group No. 121

DOE Office of Scientific and Technical Information (OSTI.GOV)

Thomadsen, Bruce R.; Thompson, Heaton H. II; Jani, Shirish K.

Medical products (devices, drugs, or biologics) contain information in their labeling regarding the manner in which the manufacturer has determined that the products can be used in a safe and effective manner. The Food and Drug Administration (FDA) approves medical products for use for these specific indications which are part of the medical product's labeling. When medical products are used in a manner not specified in the labeling, it is commonly referred to as off-label use. The practice of medicine allows for this off-label use to treat individual patients, but the ethical and legal implications for such unapproved use canmore » be confusing. Although the responsibility and, ultimately, the liability for off-label use often rests with the prescribing physician, medical physicists and others are also responsible for the safe and proper use of the medical products. When these products are used for purposes other than which they were approved, it is important for medical physicists to understand their responsibilities. In the United States, medical products can only be marketed if officially cleared, approved, or licensed by the FDA; they can be used if they are not subject to or specifically exempt from FDA regulations, or if they are being used in research with the appropriate regulatory safeguards. Medical devices are either cleared or approved by FDA's Center for Devices and Radiological Health. Drugs are approved by FDA's Center for Drug Evaluation and Research, and biological products such as vaccines or blood are licensed under a biologics license agreement by FDA's Center for Biologics Evaluation and Research. For the purpose of this report, the process by which the FDA eventually clears, approves, or licenses such products for marketing in the United States will be referred to as approval. This report summarizes the various ways medical products, primarily medical devices, can legally be brought to market in the United States, and includes a discussion

Label-free imaging of cellular malformation using high resolution photoacoustic microscopy

NASA Astrophysics Data System (ADS)

Chen, Zhongjiang; Li, Bingbing; Yang, Sihua

2014-09-01

A label-free high resolution photoacoustic microscopy (PAM) system for imaging cellular malformation is presented. The carbon fibers were used to testify the lateral resolution of the PAM. Currently, the lateral resolution is better than 2.7 μm. The human normal red blood cells (RBCs) were used to prove the imaging capability of the system, and a single red blood cell was mapped with high contrast. Moreover, the iron deficiency anemia RBCs were clearly distinguished from the cell morphology by using the PAM. The experimental results demonstrate that the photoacoustic microscopy system can accomplish label-free photoacoustic imaging and that it has clinical potential for use in the detection of erythrocytes and blood vessels malformation.

Labeling transplanted mice islet with polyvinylpyrrolidone coated superparamagnetic iron oxide nanoparticles for in vivo detection by magnetic resonance imaging

NASA Astrophysics Data System (ADS)

Huang, Hai; Xie, Qiuping; Kang, Muxing; Zhang, Bo; Zhang, Hui; Chen, Jin; Zhai, Chuanxin; Yang, Deren; Jiang, Biao; Wu, Yulian

2009-09-01

Superparamagnetic iron oxide nanoparticles (SPIO) are emerging as a novel probe for noninvasive cell tracking with magnetic resonance imaging (MRI) and have potential wide usage in medical research. In this study, we have developed a method using high-temperature hydrolysis of chelate metal alkoxide complexes to synthesize polyvinylpyrrolidone coated iron oxide nanoparticles (PVP-SPIO), as a biocompatible magnetic agent that can efficiently label mice islet β-cells. The size, crystal structure and magnetic properties of the as-synthesized nanoparticles have been characterized. The newly synthesized PVP-SPIO with high stability, crystallinity and saturation magnetization can be efficiently internalized into β-cells, without affecting viability and function. The imaging of 100 PVP-SPIO-labeled mice islets in the syngeneic renal subcapsular model of transplantation under a clinical 3.0 T MR imager showed high spatial resolution in vivo. These results indicated the great potential application of the PVP-SPIO as an MRI contrast agent for monitoring transplanted islet grafts in the clinical management of diabetes in the near future.

Compressive sensing in medical imaging

PubMed Central

Graff, Christian G.; Sidky, Emil Y.

2015-01-01

The promise of compressive sensing, exploitation of compressibility to achieve high quality image reconstructions with less data, has attracted a great deal of attention in the medical imaging community. At the Compressed Sensing Incubator meeting held in April 2014 at OSA Headquarters in Washington, DC, presentations were given summarizing some of the research efforts ongoing in compressive sensing for x-ray computed tomography and magnetic resonance imaging systems. This article provides an expanded version of these presentations. Sparsity-exploiting reconstruction algorithms that have gained popularity in the medical imaging community are studied, and examples of clinical applications that could benefit from compressive sensing ideas are provided. The current and potential future impact of compressive sensing on the medical imaging field is discussed. PMID:25968400

Segmentation of Vasculature from Fluorescently Labeled Endothelial Cells in Multi-Photon Microscopy Images.

PubMed

Bates, Russell; Irving, Benjamin; Markelc, Bostjan; Kaeppler, Jakob; Brown, Graham; Muschel, Ruth J; Brady, Sir Michael; Grau, Vicente; Schnabel, Julia A

2017-08-09

Vasculature is known to be of key biological significance, especially in the study of tumors. As such, considerable effort has been focused on the automated segmentation of vasculature in medical and pre-clinical images. The majority of vascular segmentation methods focus on bloodpool labeling methods, however, particularly in the study of tumors it is of particular interest to be able to visualize both perfused and non-perfused vasculature. Imaging vasculature by highlighting the endothelium provides a way to separate the morphology of vasculature from the potentially confounding factor of perfusion. Here we present a method for the segmentation of tumor vasculature in 3D fluorescence microscopy images using signals from the endothelial and surrounding cells. We show that our method can provide complete and semantically meaningful segmentations of complex vasculature using a supervoxel-Markov Random Field approach. We show that in terms of extracting meaningful segmentations of the vasculature, our method out-performs both a state-ofthe- art method, specific to these data, as well as more classical vasculature segmentation methods.

Advancements of labelled radio-pharmaceutics imaging with the PIM-MPGD

NASA Astrophysics Data System (ADS)

Donnard, J.; Arlicot, N.; Berny, R.; Carduner, H.; Leray, P.; Morteau, E.; Servagent, N.; Thers, D.

2009-11-01

The Beta autoradiography is widely used in pharmacology or in biological fields to study the response of an organism to a certain kind of molecule. The image of the distribution is processed by studying the concentration of the radioactivity into different organs. We report on the development of an integrated apparatus based on a PIM device (Parallel Ionization Multiplier) able to process the image of 10 microscope slides at the same time over an area of 18*18 cm2. Thanks to a vacuum pump and a regulation gas circuit, 5 minutes is sufficient to begin an acquisition. All the electronics and the gas distribution are included in the structure leading to a transportable device. Special software has been developed to process data in real time with image visualization. Biological samples can be labelled with β emitters of low energy like 3H/14C or Auger electrons of 125I/99mTc. The measured spatial resolution is 30 μm in 3H and the trigger and the charge rate are constant over more than 6 days of acquisition showing good stability of the device. Moreover, collaboration with doctors and biologists of INSERM (National Institute for Medical Research in France) has started in order to demonstrate that MPGD's can be easily proposed outside a physics laboratory.

Firefly Luciferin-Inspired Biocompatible Chemistry for Protein Labeling and In Vivo Imaging.

PubMed

Wang, Yuqi; An, Ruibing; Luo, Zhiliang; Ye, Deju

2018-04-17

Biocompatible reactions have emerged as versatile tools to build various molecular imaging probes that hold great promise for the detection of biological processes in vitro and/or in vivo. In this Minireview, we describe the recent advances in the development of a firefly luciferin-inspired biocompatible reaction between cyanobenzothiazole (CBT) and cysteine (Cys), and highlight its versatility to label proteins and build multimodality molecular imaging probes. The review starts from the general introduction of biocompatible reactions, which is followed by briefly describing the development of the firefly luciferin-inspired biocompatible chemistry. We then discuss its applications for the specific protein labeling and for the development of multimodality imaging probes (fluorescence, bioluminescence, MRI, PET, photoacoustic, etc.) that enable high sensitivity and spatial resolution imaging of redox environment, furin and caspase-3/7 activity in living cells and mice. Finally, we offer the conclusions and our perspective on the various and potential applications of this reaction. We hope that this review will contribute to the research of biocompatible reactions for their versatile applications in protein labeling and molecular imaging. © 2018 Wiley-VCH Verlag GmbH & Co. KGaA, Weinheim.

Labeling of Medication and Placebo Alters the Outcome of Episodic Migraine Attacks

PubMed Central

Kam-Hansen, Slavenka; Jakubowski, Moshe; Kelley, John M.; Kirsch, Irving; Hoaglin, David C.; Kaptchuk, Ted J.; Burstein, Rami

2014-01-01

Information provided to patients is thought to influence placebo and drug effects. We investigated the potential relationship between treatment labeling and its outcome in a prospective, within-subjects, repeated measures study of episodic migraine. A cohort of 66 participants documented 7 separate migraine attack: one untreated attack, followed by six attacks that were randomly assigned for either rizatriptan (10 mg Maxalt) or placebo treatments, each of which labeled once as ‘Maxalt’, once as ‘Placebo’, and once as ‘Maxalt or Placebo’ (459 documented attacks). Data were analyzed using generalized linear mixed model statistics. While Maxalt was generally superior to placebo, the placebo effect, and to a lesser extent Maxalt efficacy, increased monotonically with treatment labeling as follows: ‘Placebo’ label < ‘Maxalt or placebo’ label ≤ ‘Maxalt’ label. Efficacy of Maxalt mislabeled as placebo was not significantly different from the efficacy of placebo mislabeled as Maxalt. The placebo effect was significant under each labeling condition relative to no treatment, amounting in magnitude to >50% of Maxalt effect under the corresponding labeling condition. Thus, incremental “positive” information yielded incremental efficacy of placebo and medication during migraine attacks. PMID:24401940

Machine Learning for Medical Imaging

PubMed Central

Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L.

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. ©RSNA, 2017 PMID:28212054

Machine Learning for Medical Imaging.

PubMed

Erickson, Bradley J; Korfiatis, Panagiotis; Akkus, Zeynettin; Kline, Timothy L

2017-01-01

Machine learning is a technique for recognizing patterns that can be applied to medical images. Although it is a powerful tool that can help in rendering medical diagnoses, it can be misapplied. Machine learning typically begins with the machine learning algorithm system computing the image features that are believed to be of importance in making the prediction or diagnosis of interest. The machine learning algorithm system then identifies the best combination of these image features for classifying the image or computing some metric for the given image region. There are several methods that can be used, each with different strengths and weaknesses. There are open-source versions of most of these machine learning methods that make them easy to try and apply to images. Several metrics for measuring the performance of an algorithm exist; however, one must be aware of the possible associated pitfalls that can result in misleading metrics. More recently, deep learning has started to be used; this method has the benefit that it does not require image feature identification and calculation as a first step; rather, features are identified as part of the learning process. Machine learning has been used in medical imaging and will have a greater influence in the future. Those working in medical imaging must be aware of how machine learning works. © RSNA, 2017.

Novel Algorithm for Classification of Medical Images

NASA Astrophysics Data System (ADS)

Bhushan, Bharat; Juneja, Monika

2010-11-01

Content-based image retrieval (CBIR) methods in medical image databases have been designed to support specific tasks, such as retrieval of medical images. These methods cannot be transferred to other medical applications since different imaging modalities require different types of processing. To enable content-based queries in diverse collections of medical images, the retrieval system must be familiar with the current Image class prior to the query processing. Further, almost all of them deal with the DICOM imaging format. In this paper a novel algorithm based on energy information obtained from wavelet transform for the classification of medical images according to their modalities is described. For this two types of wavelets have been used and have been shown that energy obtained in either case is quite distinct for each of the body part. This technique can be successfully applied to different image formats. The results are shown for JPEG imaging format.

Mobile, Multi-modal, Label-Free Imaging Probe Analysis of Choroidal Oximetry and Retinal Hypoxia

DTIC Science & Technology

2015-10-01

eyes and image choroidal vessels/capillaries using CARS intravital microscopy Subtask 3: Measure oxy-hemoglobin levels in PBI test and control eyes...AWARD NUMBER: W81XWH-14-1-0537 TITLE: Mobile, Multi-modal, Label-Free Imaging Probe Analysis of Choroidal Oximetry and Retinal Hypoxia...4. TITLE AND SUBTITLE Mobile, Multimodal, Label-Free Imaging Probe Analysis of Choroidal Oximetry and Retinal Hypoxia 5a. CONTRACT NUMBER W81XWH

Magnetic Resonance Imaging of Iron Oxide-Labeled Human Embryonic Stem Cell-Derived Cardiac Progenitors.

PubMed

Skelton, Rhys J P; Khoja, Suhail; Almeida, Shone; Rapacchi, Stanislas; Han, Fei; Engel, James; Zhao, Peng; Hu, Peng; Stanley, Edouard G; Elefanty, Andrew G; Kwon, Murray; Elliott, David A; Ardehali, Reza

2016-01-01

Given the limited regenerative capacity of the heart, cellular therapy with stem cell-derived cardiac cells could be a potential treatment for patients with heart disease. However, reliable imaging techniques to longitudinally assess engraftment of the transplanted cells are scant. To address this issue, we used ferumoxytol as a labeling agent of human embryonic stem cell-derived cardiac progenitor cells (hESC-CPCs) to facilitate tracking by magnetic resonance imaging (MRI) in a large animal model. Differentiating hESCs were exposed to ferumoxytol at different time points and varying concentrations. We determined that treatment with ferumoxytol at 300 μg/ml on day 0 of cardiac differentiation offered adequate cell viability and signal intensity for MRI detection without compromising further differentiation into definitive cardiac lineages. Labeled hESC-CPCs were transplanted by open surgical methods into the left ventricular free wall of uninjured pig hearts and imaged both ex vivo and in vivo. Comprehensive T2*-weighted images were obtained immediately after transplantation and 40 days later before termination. The localization and dispersion of labeled cells could be effectively imaged and tracked at days 0 and 40 by MRI. Thus, under the described conditions, ferumoxytol can be used as a long-term, differentiation-neutral cell-labeling agent to track transplanted hESC-CPCs in vivo using MRI. The development of a safe and reproducible in vivo imaging technique to track the fate of transplanted human embryonic stem cell-derived cardiac progenitor cells (hESC-CPCs) is a necessary step to clinical translation. An iron oxide nanoparticle (ferumoxytol)-based approach was used for cell labeling and subsequent in vivo magnetic resonance imaging monitoring of hESC-CPCs transplanted into uninjured pig hearts. The present results demonstrate the use of ferumoxytol labeling and imaging techniques in tracking the location and dispersion of cell grafts, highlighting its

Labeling and Magnetic Resonance Imaging of Exosomes Isolated from Adipose Stem Cells.

PubMed

Busato, Alice; Bonafede, Roberta; Bontempi, Pietro; Scambi, Ilaria; Schiaffino, Lorenzo; Benati, Donatella; Malatesta, Manuela; Sbarbati, Andrea; Marzola, Pasquina; Mariotti, Raffaella

2017-06-19

Adipose stem cells (ASC) represent a promising therapeutic approach for neurodegenerative diseases. Most biological effects of ASC are probably mediated by extracellular vesicles, such as exosomes, which influence the surrounding cells. Current development of exosome therapies requires efficient and noninvasive methods to localize, monitor, and track the exosomes. Among imaging methods used for this purpose, magnetic resonance imaging (MRI) has advantages: high spatial resolution, rapid in vivo acquisition, and radiation-free operation. To be detectable with MRI, exosomes must be labeled with MR contrast agents, such as ultra-small superparamagnetic iron oxide nanoparticles (USPIO). Here, we set up an innovative approach for exosome labeling that preserves their morphology and physiological characteristics. We show that by labeling ASC with USPIO before extraction of nanovesicles, the isolated exosomes retain nanoparticles and can be visualized by MRI. The current work aims at validating this novel USPIO-based exosome labeling method by monitoring the efficiency of the labeling with MRI both in ASC and in exosomes. © 2017 by John Wiley & Sons, Inc. Copyright © 2017 John Wiley & Sons, Inc.

The algorithm stitching for medical imaging

NASA Astrophysics Data System (ADS)

Semenishchev, E.; Marchuk, V.; Voronin, V.; Pismenskova, M.; Tolstova, I.; Svirin, I.

2016-05-01

In this paper we propose a stitching algorithm of medical images into one. The algorithm is designed to stitching the medical x-ray imaging, biological particles in microscopic images, medical microscopic images and other. Such image can improve the diagnosis accuracy and quality for minimally invasive studies (e.g., laparoscopy, ophthalmology and other). The proposed algorithm is based on the following steps: the searching and selection areas with overlap boundaries; the keypoint and feature detection; the preliminary stitching images and transformation to reduce the visible distortion; the search a single unified borders in overlap area; brightness, contrast and white balance converting; the superimposition into a one image. Experimental results demonstrate the effectiveness of the proposed method in the task of image stitching.

Medical imaging systems

DOEpatents

Frangioni, John V

2013-06-25

A medical imaging system provides simultaneous rendering of visible light and diagnostic or functional images. The system may be portable, and may include adapters for connecting various light sources and cameras in open surgical environments or laparascopic or endoscopic environments. A user interface provides control over the functionality of the integrated imaging system. In one embodiment, the system provides a tool for surgical pathology.

Style-independent document labeling: design and performance evaluation

NASA Astrophysics Data System (ADS)

Mao, Song; Kim, Jong Woo; Thoma, George R.

2003-12-01

The Medical Article Records System or MARS has been developed at the U.S. National Library of Medicine (NLM) for automated data entry of bibliographical information from medical journals into MEDLINE, the premier bibliographic citation database at NLM. Currently, a rule-based algorithm (called ZoneCzar) is used for labeling important bibliographical fields (title, author, affiliation, and abstract) on medical journal article page images. While rules have been created for medical journals with regular layout types, new rules have to be manually created for any input journals with arbitrary or new layout types. Therefore, it is of interest to label any journal articles independent of their layout styles. In this paper, we first describe a system (called ZoneMatch) for automated generation of crucial geometric and non-geometric features of important bibliographical fields based on string-matching and clustering techniques. The rule based algorithm is then modified to use these features to perform style-independent labeling. We then describe a performance evaluation method for quantitatively evaluating our algorithm and characterizing its error distributions. Experimental results show that the labeling performance of the rule-based algorithm is significantly improved when the generated features are used.

Systems-level analysis of microbial community organization through combinatorial labeling and spectral imaging.

PubMed

Valm, Alex M; Mark Welch, Jessica L; Rieken, Christopher W; Hasegawa, Yuko; Sogin, Mitchell L; Oldenbourg, Rudolf; Dewhirst, Floyd E; Borisy, Gary G

2011-03-08

Microbes in nature frequently function as members of complex multitaxon communities, but the structural organization of these communities at the micrometer level is poorly understood because of limitations in labeling and imaging technology. We report here a combinatorial labeling strategy coupled with spectral image acquisition and analysis that greatly expands the number of fluorescent signatures distinguishable in a single image. As an imaging proof of principle, we first demonstrated visualization of Escherichia coli labeled by fluorescence in situ hybridization (FISH) with 28 different binary combinations of eight fluorophores. As a biological proof of principle, we then applied this Combinatorial Labeling and Spectral Imaging FISH (CLASI-FISH) strategy using genus- and family-specific probes to visualize simultaneously and differentiate 15 different phylotypes in an artificial mixture of laboratory-grown microbes. We then illustrated the utility of our method for the structural analysis of a natural microbial community, namely, human dental plaque, a microbial biofilm. We demonstrate that 15 taxa in the plaque community can be imaged simultaneously and analyzed and that this community was dominated by early colonizers, including species of Streptococcus, Prevotella, Actinomyces, and Veillonella. Proximity analysis was used to determine the frequency of inter- and intrataxon cell-to-cell associations which revealed statistically significant intertaxon pairings. Cells of the genera Prevotella and Actinomyces showed the most interspecies associations, suggesting a central role for these genera in establishing and maintaining biofilm complexity. The results provide an initial systems-level structural analysis of biofilm organization.

The Protein Corona around Nanoparticles Facilitates Stem Cell Labeling for Clinical MR Imaging.

PubMed

Nejadnik, Hossein; Taghavi-Garmestani, Seyed-Meghdad; Madsen, Steven J; Li, Kai; Zanganeh, Saeid; Yang, Phillip; Mahmoudi, Morteza; Daldrup-Link, Heike E

2018-03-01

Purpose To evaluate if the formation of a protein corona around ferumoxytol nanoparticles can facilitate stem cell labeling for in vivo tracking with magnetic resonance (MR) imaging. Materials and Methods Ferumoxytol was incubated in media containing human serum (group 1), fetal bovine serum (group 2), StemPro medium (group 3), protamine (group 4), and protamine plus heparin (group 5). Formation of a protein corona was characterized by means of dynamic light scattering, ζ potential, and liquid chromatography-mass spectrometry. Iron uptake was evaluated with 3,3'-diaminobenzidine-Prussian blue staining, lysosomal staining, and inductively coupled plasma spectrometry. To evaluate the effect of a protein corona on stem cell labeling, human mesenchymal stem cells (hMSCs) were labeled with the above formulations, implanted into pig knee specimens, and investigated with T2-weighted fast spin-echo and multiecho spin-echo sequences on a 3.0-T MR imaging unit. Data in different groups were compared by using a Kruskal-Wallis test. Results Compared with bare nanoparticles, all experimental groups showed significantly increased negative ζ values (from -37 to less than -10; P = .008). Nanoparticles in groups 1-3 showed an increased size because of the formation of a protein corona. hMSCs labeled with group 1-5 media showed significantly shortened T2 relaxation times compared with unlabeled control cells (P = .0012). hMSCs labeled with group 3 and 5 media had the highest iron uptake after cells labeled with group 1 medium. After implantation into pig knees, hMSCs labeled with group 1 medium showed significantly shorter T2 relaxation times than hMSCs labeled with group 2-5 media (P = .0022). Conclusion The protein corona around ferumoxytol nanoparticles can facilitate stem cell labeling for clinical cell tracking with MR imaging. © RSNA, 2017 Online supplemental material is available for this article.

Medical image analysis with artificial neural networks.

PubMed

Jiang, J; Trundle, P; Ren, J

2010-12-01

Given that neural networks have been widely reported in the research community of medical imaging, we provide a focused literature survey on recent neural network developments in computer-aided diagnosis, medical image segmentation and edge detection towards visual content analysis, and medical image registration for its pre-processing and post-processing, with the aims of increasing awareness of how neural networks can be applied to these areas and to provide a foundation for further research and practical development. Representative techniques and algorithms are explained in detail to provide inspiring examples illustrating: (i) how a known neural network with fixed structure and training procedure could be applied to resolve a medical imaging problem; (ii) how medical images could be analysed, processed, and characterised by neural networks; and (iii) how neural networks could be expanded further to resolve problems relevant to medical imaging. In the concluding section, a highlight of comparisons among many neural network applications is included to provide a global view on computational intelligence with neural networks in medical imaging. Copyright © 2010 Elsevier Ltd. All rights reserved.

Nanotechnology-supported THz medical imaging

PubMed Central

Stylianou, Andreas; Talias, Michael A

2013-01-01

Over the last few decades, the achievements and progress in the field of medical imaging have dramatically enhanced the early detection and treatment of many pathological conditions. The development of new imaging modalities, especially non-ionising ones, which will improve prognosis, is of crucial importance. A number of novel imaging modalities have been developed but they are still in the initial stages of development and serious drawbacks obstruct them from offering their benefits to the medical field. In the 21 st century, it is believed that nanotechnology will highly influence our everyday life and dramatically change the world of medicine, including medical imaging. Here we discuss how nanotechnology, which is still in its infancy, can improve Terahertz (THz) imaging, an emerging imaging modality, and how it may find its way into real clinical applications. THz imaging is characterised by the use of non-ionising radiation and although it has the potential to be used in many biomedical fields, it remains in the field of basic research. An extensive review of the recent available literature shows how the current state of this emerging imaging modality can be transformed by nanotechnology. Innovative scientific concepts that use nanotechnology-based techniques to overcome some of the limitations of the use of THz imaging are discussed. We review a number of drawbacks, such as a low contrast mechanism, poor source performance and bulky THz systems, which characterise present THz medical imaging and suggest how they can be overcome through nanotechnology. Better resolution and higher detection sensitivity can also be achieved using nanotechnology techniques. PMID:24555052

[Medical image compression: a review].

PubMed

Noreña, Tatiana; Romero, Eduardo

2013-01-01

Modern medicine is an increasingly complex activity , based on the evidence ; it consists of information from multiple sources : medical record text , sound recordings , images and videos generated by a large number of devices . Medical imaging is one of the most important sources of information since they offer comprehensive support of medical procedures for diagnosis and follow-up . However , the amount of information generated by image capturing gadgets quickly exceeds storage availability in radiology services , generating additional costs in devices with greater storage capacity . Besides , the current trend of developing applications in cloud computing has limitations, even though virtual storage is available from anywhere, connections are made through internet . In these scenarios the optimal use of information necessarily requires powerful compression algorithms adapted to medical activity needs . In this paper we present a review of compression techniques used for image storage , and a critical analysis of them from the point of view of their use in clinical settings.

Label-free imaging of brain and brain tumor specimens with combined two-photon excited fluorescence and second harmonic generation microscopy

NASA Astrophysics Data System (ADS)

Jiang, Liwei; Wang, Xingfu; Wu, Zanyi; Du, Huiping; Wang, Shu; Li, Lianhuang; Fang, Na; Lin, Peihua; Chen, Jianxin; Kang, Dezhi; Zhuo, Shuangmu

2017-10-01

Label-free imaging techniques are gaining acceptance within the medical imaging field, including brain imaging, because they have the potential to be applied to intraoperative in situ identifications of pathological conditions. In this paper, we describe the use of two-photon excited fluorescence (TPEF) and second harmonic generation (SHG) microscopy in combination for the label-free detection of brain and brain tumor specimens; gliomas. Two independently detecting channels were chosen to subsequently collect TPEF/SHG signals from the specimen to increase TPEF/SHG image contrasts. Our results indicate that the combined TPEF/SHG microscopic techniques can provide similar rat brain structural information and produce a similar resolution like conventional H&E staining in neuropathology; including meninges, cerebral cortex, white-matter structure corpus callosum, choroid plexus, hippocampus, striatum, and cerebellar cortex. It can simultaneously detect infiltrating human brain tumor cells, the extracellular matrix collagen fiber of connective stroma within brain vessels and collagen depostion in tumor microenvironments. The nuclear-to-cytoplasmic ratio and collagen content can be extracted as quantitative indicators for differentiating brain gliomas from healthy brain tissues. With the development of two-photon fiberscopes and microendoscope probes and their clinical applications, the combined TPEF and SHG microcopy may become an important multimodal, nonlinear optical imaging approach for real-time intraoperative histological diagnostics of residual brain tumors. These occur in various brain regions during ongoing surgeries through the method of simultaneously identifying tumor cells, and the change of tumor microenvironments, without the need for the removal biopsies and without the need for tissue labelling or fluorescent markers.

Clinical application of 3D arterial spin-labeled brain perfusion imaging for Alzheimer disease: comparison with brain perfusion SPECT.

PubMed

Takahashi, H; Ishii, K; Hosokawa, C; Hyodo, T; Kashiwagi, N; Matsuki, M; Ashikaga, R; Murakami, T

2014-05-01

Alzheimer disease is the most common neurodegenerative disorder with dementia, and a practical and economic biomarker for diagnosis of Alzheimer disease is needed. Three-dimensional arterial spin-labeling, with its high signal-to-noise ratio, enables measurement of cerebral blood flow precisely without any extrinsic tracers. We evaluated the performance of 3D arterial spin-labeling compared with SPECT, and demonstrated the 3D arterial spin-labeled imaging characteristics in the diagnosis of Alzheimer disease. This study included 68 patients with clinically suspected Alzheimer disease who underwent both 3D arterial spin-labeling and SPECT imaging. Two readers independently assessed both images. Kendall W coefficients of concordance (K) were computed, and receiver operating characteristic analyses were performed for each reader. The differences between the images in regional perfusion distribution were evaluated by means of statistical parametric mapping, and the incidence of hypoperfusion of the cerebral watershed area, referred to as "borderzone sign" in the 3D arterial spin-labeled images, was determined. Readers showed K = 0.82/0.73 for SPECT/3D arterial spin-labeled imaging, and the respective areas under the receiver operating characteristic curve were 0.82/0.69 for reader 1 and 0.80/0.69 for reader 2. Statistical parametric mapping showed that the perisylvian and medial parieto-occipital perfusion in the arterial spin-labeled images was significantly higher than that in the SPECT images. Borderzone sign was observed on 3D arterial spin-labeling in 70% of patients misdiagnosed with Alzheimer disease. The diagnostic performance of 3D arterial spin-labeling and SPECT for Alzheimer disease was almost equivalent. Three-dimensional arterial spin-labeled imaging was more influenced by hemodynamic factors than was SPECT imaging. © 2014 by American Journal of Neuroradiology.

Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy

NASA Astrophysics Data System (ADS)

Ben Arous, Juliette; Binding, Jonas; Léger, Jean-François; Casado, Mariano; Topilko, Piotr; Gigan, Sylvain; Claude Boccara, A.; Bourdieu, Laurent

2011-11-01

Myelin sheath disruption is responsible for multiple neuropathies in the central and peripheral nervous system. Myelin imaging has thus become an important diagnosis tool. However, in vivo imaging has been limited to either low-resolution techniques unable to resolve individual fibers or to low-penetration imaging of single fibers, which cannot provide quantitative information about large volumes of tissue, as required for diagnostic purposes. Here, we perform myelin imaging without labeling and at micron-scale resolution with >300-μm penetration depth on living rodents. This was achieved with a prototype [termed deep optical coherence microscopy (deep-OCM)] of a high-numerical aperture infrared full-field optical coherence microscope, which includes aberration correction for the compensation of refractive index mismatch and high-frame-rate interferometric measurements. We were able to measure the density of individual myelinated fibers in the rat cortex over a large volume of gray matter. In the peripheral nervous system, deep-OCM allows, after minor surgery, in situ imaging of single myelinated fibers over a large fraction of the sciatic nerve. This allows quantitative comparison of normal and Krox20 mutant mice, in which myelination in the peripheral nervous system is impaired. This opens promising perspectives for myelin chronic imaging in demyelinating diseases and for minimally invasive medical diagnosis.

Single myelin fiber imaging in living rodents without labeling by deep optical coherence microscopy.

PubMed

Ben Arous, Juliette; Binding, Jonas; Léger, Jean-François; Casado, Mariano; Topilko, Piotr; Gigan, Sylvain; Boccara, A Claude; Bourdieu, Laurent

2011-11-01

Myelin sheath disruption is responsible for multiple neuropathies in the central and peripheral nervous system. Myelin imaging has thus become an important diagnosis tool. However, in vivo imaging has been limited to either low-resolution techniques unable to resolve individual fibers or to low-penetration imaging of single fibers, which cannot provide quantitative information about large volumes of tissue, as required for diagnostic purposes. Here, we perform myelin imaging without labeling and at micron-scale resolution with >300-μm penetration depth on living rodents. This was achieved with a prototype [termed deep optical coherence microscopy (deep-OCM)] of a high-numerical aperture infrared full-field optical coherence microscope, which includes aberration correction for the compensation of refractive index mismatch and high-frame-rate interferometric measurements. We were able to measure the density of individual myelinated fibers in the rat cortex over a large volume of gray matter. In the peripheral nervous system, deep-OCM allows, after minor surgery, in situ imaging of single myelinated fibers over a large fraction of the sciatic nerve. This allows quantitative comparison of normal and Krox20 mutant mice, in which myelination in the peripheral nervous system is impaired. This opens promising perspectives for myelin chronic imaging in demyelinating diseases and for minimally invasive medical diagnosis.

Image analysis and modeling in medical image computing. Recent developments and advances.

PubMed

Handels, H; Deserno, T M; Meinzer, H-P; Tolxdorff, T

2012-01-01

Medical image computing is of growing importance in medical diagnostics and image-guided therapy. Nowadays, image analysis systems integrating advanced image computing methods are used in practice e.g. to extract quantitative image parameters or to support the surgeon during a navigated intervention. However, the grade of automation, accuracy, reproducibility and robustness of medical image computing methods has to be increased to meet the requirements in clinical routine. In the focus theme, recent developments and advances in the field of modeling and model-based image analysis are described. The introduction of models in the image analysis process enables improvements of image analysis algorithms in terms of automation, accuracy, reproducibility and robustness. Furthermore, model-based image computing techniques open up new perspectives for prediction of organ changes and risk analysis of patients. Selected contributions are assembled to present latest advances in the field. The authors were invited to present their recent work and results based on their outstanding contributions to the Conference on Medical Image Computing BVM 2011 held at the University of Lübeck, Germany. All manuscripts had to pass a comprehensive peer review. Modeling approaches and model-based image analysis methods showing new trends and perspectives in model-based medical image computing are described. Complex models are used in different medical applications and medical images like radiographic images, dual-energy CT images, MR images, diffusion tensor images as well as microscopic images are analyzed. The applications emphasize the high potential and the wide application range of these methods. The use of model-based image analysis methods can improve segmentation quality as well as the accuracy and reproducibility of quantitative image analysis. Furthermore, image-based models enable new insights and can lead to a deeper understanding of complex dynamic mechanisms in the human body

Mobile medical image retrieval

NASA Astrophysics Data System (ADS)

Duc, Samuel; Depeursinge, Adrien; Eggel, Ivan; Müller, Henning

2011-03-01

Images are an integral part of medical practice for diagnosis, treatment planning and teaching. Image retrieval has gained in importance mainly as a research domain over the past 20 years. Both textual and visual retrieval of images are essential. In the process of mobile devices becoming reliable and having a functionality equaling that of formerly desktop clients, mobile computing has gained ground and many applications have been explored. This creates a new field of mobile information search & access and in this context images can play an important role as they often allow understanding complex scenarios much quicker and easier than free text. Mobile information retrieval in general has skyrocketed over the past year with many new applications and tools being developed and all sorts of interfaces being adapted to mobile clients. This article describes constraints of an information retrieval system including visual and textual information retrieval from the medical literature of BioMedCentral and of the RSNA journals Radiology and Radiographics. Solutions for mobile data access with an example on an iPhone in a web-based environment are presented as iPhones are frequently used and the operating system is bound to become the most frequent smartphone operating system in 2011. A web-based scenario was chosen to allow for a use by other smart phone platforms such as Android as well. Constraints of small screens and navigation with touch screens are taken into account in the development of the application. A hybrid choice had to be taken to allow for taking pictures with the cell phone camera and upload them for visual similarity search as most producers of smart phones block this functionality to web applications. Mobile information access and in particular access to images can be surprisingly efficient and effective on smaller screens. Images can be read on screen much faster and relevance of documents can be identified quickly through the use of images contained in

21 CFR 892.2020 - Medical image communications device.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Medical image communications device. 892.2020... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2020 Medical image communications device. (a) Identification. A medical image communications device provides electronic transfer of medical...

21 CFR 892.2020 - Medical image communications device.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Medical image communications device. 892.2020... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2020 Medical image communications device. (a) Identification. A medical image communications device provides electronic transfer of medical...

21 CFR 892.2020 - Medical image communications device.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Medical image communications device. 892.2020... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2020 Medical image communications device. (a) Identification. A medical image communications device provides electronic transfer of medical...

21 CFR 892.2020 - Medical image communications device.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Medical image communications device. 892.2020... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2020 Medical image communications device. (a) Identification. A medical image communications device provides electronic transfer of medical...

Medical hyperspectral imaging: a review

PubMed Central

Lu, Guolan; Fei, Baowei

2014-01-01

Abstract. Hyperspectral imaging (HSI) is an emerging imaging modality for medical applications, especially in disease diagnosis and image-guided surgery. HSI acquires a three-dimensional dataset called hypercube, with two spatial dimensions and one spectral dimension. Spatially resolved spectral imaging obtained by HSI provides diagnostic information about the tissue physiology, morphology, and composition. This review paper presents an overview of the literature on medical hyperspectral imaging technology and its applications. The aim of the survey is threefold: an introduction for those new to the field, an overview for those working in the field, and a reference for those searching for literature on a specific application. PMID:24441941

DICOM: a standard for medical imaging

NASA Astrophysics Data System (ADS)

Horii, Steven C.; Bidgood, W. Dean

1993-01-01

Since 1983, the American College of Radiology (ACR) and the National Electrical Manufacturers Association (NEMA) have been engaged in developing standards related to medical imaging. This alliance of users and manufacturers was formed to meet the needs of the medical imaging community as its use of digital imaging technology increased. The development of electronic picture archiving and communications systems (PACS), which could connect a number of medical imaging devices together in a network, led to the need for a standard interface and data structure for use on imaging equipment. Since medical image files tend to be very large and include much text information along with the image, the need for a fast, flexible, and extensible standard was quickly established. The ACR-NEMA Digital Imaging and Communications Standards Committee developed a standard which met these needs. The standard (ACR-NEMA 300-1988) was first published in 1985 and revised in 1988. It is increasingly available from equipment manufacturers. The current work of the ACR- NEMA Committee has been to extend the standard to incorporate direct network connection features, and build on standards work done by the International Standards Organization in its Open Systems Interconnection series. This new standard, called Digital Imaging and Communication in Medicine (DICOM), follows an object-oriented design methodology and makes use of as many existing internationally accepted standards as possible. This paper gives a brief overview of the requirements for communications standards in medical imaging, a history of the ACR-NEMA effort and what it has produced, and a description of the DICOM standard.

Watermarking and copyright labeling of printed images

NASA Astrophysics Data System (ADS)

Hel-Or, Hagit Z.

2001-07-01

Digital watermarking is a labeling technique for digital images which embeds a code into the digital data so the data are marked. Watermarking techniques previously developed deal with on-line digital data. These techniques have been developed to withstand digital attacks such as image processing, image compression and geometric transformations. However, one must also consider the readily available attack of printing and scanning. The available watermarking techniques are not reliable under printing and scanning. In fact, one must consider the availability of watermarks for printed images as well as for digital images. An important issue is to intercept and prevent forgery in printed material such as currency notes, back checks, etc. and to track and validate sensitive and secrete printed material. Watermarking in such printed material can be used not only for verification of ownership but as an indicator of date and type of transaction or date and source of the printed data. In this work we propose a method of embedding watermarks in printed images by inherently taking advantage of the printing process. The method is visually unobtrusive to the printed image, the watermark is easily extracted and is robust under reconstruction errors. The decoding algorithm is automatic given the watermarked image.

21 CFR 892.2040 - Medical image hardcopy device.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Medical image hardcopy device. 892.2040 Section... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2040 Medical image hardcopy device. (a) Identification. A medical image hardcopy device is a device that produces a visible printed record of a medical...

21 CFR 892.2040 - Medical image hardcopy device.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Medical image hardcopy device. 892.2040 Section... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2040 Medical image hardcopy device. (a) Identification. A medical image hardcopy device is a device that produces a visible printed record of a medical...

21 CFR 892.2040 - Medical image hardcopy device.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Medical image hardcopy device. 892.2040 Section... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2040 Medical image hardcopy device. (a) Identification. A medical image hardcopy device is a device that produces a visible printed record of a medical...

21 CFR 892.2040 - Medical image hardcopy device.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Medical image hardcopy device. 892.2040 Section... (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2040 Medical image hardcopy device. (a) Identification. A medical image hardcopy device is a device that produces a visible printed record of a medical...

Multi-atlas segmentation with joint label fusion and corrective learning—an open source implementation

PubMed Central

Wang, Hongzhi; Yushkevich, Paul A.

2013-01-01

Label fusion based multi-atlas segmentation has proven to be one of the most competitive techniques for medical image segmentation. This technique transfers segmentations from expert-labeled images, called atlases, to a novel image using deformable image registration. Errors produced by label transfer are further reduced by label fusion that combines the results produced by all atlases into a consensus solution. Among the proposed label fusion strategies, weighted voting with spatially varying weight distributions derived from atlas-target intensity similarity is a simple and highly effective label fusion technique. However, one limitation of most weighted voting methods is that the weights are computed independently for each atlas, without taking into account the fact that different atlases may produce similar label errors. To address this problem, we recently developed the joint label fusion technique and the corrective learning technique, which won the first place of the 2012 MICCAI Multi-Atlas Labeling Challenge and was one of the top performers in 2013 MICCAI Segmentation: Algorithms, Theory and Applications (SATA) challenge. To make our techniques more accessible to the scientific research community, we describe an Insight-Toolkit based open source implementation of our label fusion methods. Our implementation extends our methods to work with multi-modality imaging data and is more suitable for segmentation problems with multiple labels. We demonstrate the usage of our tools through applying them to the 2012 MICCAI Multi-Atlas Labeling Challenge brain image dataset and the 2013 SATA challenge canine leg image dataset. We report the best results on these two datasets so far. PMID:24319427

Stereoscopic medical imaging collaboration system

NASA Astrophysics Data System (ADS)

Okuyama, Fumio; Hirano, Takenori; Nakabayasi, Yuusuke; Minoura, Hirohito; Tsuruoka, Shinji

2007-02-01

The computerization of the clinical record and the realization of the multimedia have brought improvement of the medical service in medical facilities. It is very important for the patients to obtain comprehensible informed consent. Therefore, the doctor should plainly explain the purpose and the content of the diagnoses and treatments for the patient. We propose and design a Telemedicine Imaging Collaboration System which presents a three dimensional medical image as X-ray CT, MRI with stereoscopic image by using virtual common information space and operating the image from a remote location. This system is composed of two personal computers, two 15 inches stereoscopic parallax barrier type LCD display (LL-151D, Sharp), one 1Gbps router and 1000base LAN cables. The software is composed of a DICOM format data transfer program, an operation program of the images, the communication program between two personal computers and a real time rendering program. Two identical images of 512×768 pixcels are displayed on two stereoscopic LCD display, and both images show an expansion, reduction by mouse operation. This system can offer a comprehensible three-dimensional image of the diseased part. Therefore, the doctor and the patient can easily understand it, depending on their needs.

Fluorescence labeled microbubbles for multimodal imaging.

PubMed

Barrefelt, Åsa; Zhao, Ying; Larsson, Malin K; Egri, Gabriella; Kuiper, Raoul V; Hamm, Jörg; Saghafian, Maryam; Caidahl, Kenneth; Brismar, Torkel B; Aspelin, Peter; Heuchel, Rainer; Muhammed, Mamoun; Dähne, Lars; Hassan, Moustapha

2015-08-28

Air-filled polyvinyl alcohol microbubbles (PVA-MBs) were recently introduced as a contrast agent for ultrasound imaging. In the present study, we explore the possibility of extending their application in multimodal imaging by labeling them with a near infrared (NIR) fluorophore, VivoTag-680. PVA-MBs were injected intravenously into FVB/N female mice and their dynamic biodistribution over 24 h was determined by 3D-fluorescence imaging co-registered with 3D-μCT imaging, to verify the anatomic location. To further confirm the biodistribution results from in vivo imaging, organs were removed and examined histologically using bright field and fluorescence microscopy. Fluorescence imaging detected PVA-MB accumulation in the lungs within the first 30 min post-injection. Redistribution to a low extent was observed in liver and kidneys at 4 h, and to a high extent mainly in the liver and spleen at 24 h. Histology confirmed PVA-MB localization in lung capillaries and macrophages. In the liver, they were associated with Kupffer cells; in the spleen, they were located mostly within the marginal-zone. Occasional MBs were observed in the kidney glomeruli and interstitium. The potential application of PVA-MBs as a contrast agent was also studied using ultrasound (US) imaging in subcutaneous and orthotopic pancreatic cancer mouse models, to visualize blood flow within the tumor mass. In conclusion, this study showed that PVA-MBs are useful as a contrast agent for multimodal imaging. Copyright © 2015 Elsevier Inc. All rights reserved.

BIRAM: a content-based image retrieval framework for medical images

NASA Astrophysics Data System (ADS)

Moreno, Ramon A.; Furuie, Sergio S.

2006-03-01

In the medical field, digital images are becoming more and more important for diagnostics and therapy of the patients. At the same time, the development of new technologies has increased the amount of image data produced in a hospital. This creates a demand for access methods that offer more than text-based queries for retrieval of the information. In this paper is proposed a framework for the retrieval of medical images that allows the use of different algorithms for the search of medical images by similarity. The framework also enables the search for textual information from an associated medical report and DICOM header information. The proposed system can be used for support of clinical decision making and is intended to be integrated with an open source picture, archiving and communication systems (PACS). The BIRAM has the following advantages: (i) Can receive several types of algorithms for image similarity search; (ii) Allows the codification of the report according to a medical dictionary, improving the indexing of the information and retrieval; (iii) The algorithms can be selectively applied to images with the appropriated characteristics, for instance, only in magnetic resonance images. The framework was implemented in Java language using a MS Access 97 database. The proposed framework can still be improved, by the use of regions of interest (ROI), indexing with slim-trees and integration with a PACS Server.

Reducing noise component on medical images

NASA Astrophysics Data System (ADS)

Semenishchev, Evgeny; Voronin, Viacheslav; Dub, Vladimir; Balabaeva, Oksana

2018-04-01

Medical visualization and analysis of medical data is an actual direction. Medical images are used in microbiology, genetics, roentgenology, oncology, surgery, ophthalmology, etc. Initial data processing is a major step towards obtaining a good diagnostic result. The paper considers the approach allows an image filtering with preservation of objects borders. The algorithm proposed in this paper is based on sequential data processing. At the first stage, local areas are determined, for this purpose the method of threshold processing, as well as the classical ICI algorithm, is applied. The second stage uses a method based on based on two criteria, namely, L2 norm and the first order square difference. To preserve the boundaries of objects, we will process the transition boundary and local neighborhood the filtering algorithm with a fixed-coefficient. For example, reconstructed images of CT, x-ray, and microbiological studies are shown. The test images show the effectiveness of the proposed algorithm. This shows the applicability of analysis many medical imaging applications.

Medical Imaging and Infertility.

PubMed

Peterson, Rebecca

2016-11-01

Infertility affects many couples, and medical imaging plays a vital role in its diagnosis and treatment. Radiologic technologists benefit from having a broad understanding of infertility risk factors and causes. This article describes the typical structure and function of the male and female reproductive systems, as well as congenital and acquired conditions that could lead to a couple's inability to conceive. Medical imaging procedures performed for infertility diagnosis are discussed, as well as common interventional options available to patients. © 2016 American Society of Radiologic Technologists.

Label-free volumetric optical imaging of intact murine brains

NASA Astrophysics Data System (ADS)

Ren, Jian; Choi, Heejin; Chung, Kwanghun; Bouma, Brett E.

2017-04-01

A central effort of today’s neuroscience is to study the brain’s ’wiring diagram’. The nervous system is believed to be a network of neurons interacting with each other through synaptic connection between axons and dendrites, therefore the neuronal connectivity map not only depicts the underlying anatomy, but also has important behavioral implications. Different approaches have been utilized to decipher neuronal circuits, including electron microscopy (EM) and light microscopy (LM). However, these approaches typically demand extensive sectioning and reconstruction for a brain sample. Recently, tissue clearing methods have enabled the investigation of a fully assembled biological system with greatly improved light penetration. Yet, most of these implementations, still require either genetic or exogenous contrast labeling for light microscopy. Here we demonstrate a high-speed approach, termed as Clearing Assisted Scattering Tomography (CAST), where intact brains can be imaged at optical resolution without labeling by leveraging tissue clearing and the scattering contrast of optical frequency domain imaging (OFDI).

Direct imaging of glycans in Arabidopsis roots via click labeling of metabolically incorporated azido-monosaccharides.

PubMed

Hoogenboom, Jorin; Berghuis, Nathalja; Cramer, Dario; Geurts, Rene; Zuilhof, Han; Wennekes, Tom

2016-10-10

Carbohydrates, also called glycans, play a crucial but not fully understood role in plant health and development. The non-template driven formation of glycans makes it impossible to image them in vivo with genetically encoded fluorescent tags and related molecular biology approaches. A solution to this problem is the use of tailor-made glycan analogs that are metabolically incorporated by the plant into its glycans. These metabolically incorporated probes can be visualized, but techniques documented so far use toxic copper-catalyzed labeling. To further expand our knowledge of plant glycobiology by direct imaging of its glycans via this method, there is need for novel click-compatible glycan analogs for plants that can be bioorthogonally labelled via copper-free techniques. Arabidopsis seedlings were incubated with azido-containing monosaccharide analogs of N-acetylglucosamine, N-acetylgalactosamine, L-fucose, and L-arabinofuranose. These azido-monosaccharides were metabolically incorporated in plant cell wall glycans of Arabidopsis seedlings. Control experiments indicated active metabolic incorporation of the azido-monosaccharide analogs into glycans rather than through non-specific absorption of the glycan analogs onto the plant cell wall. Successful copper-free labeling reactions were performed, namely an inverse-electron demand Diels-Alder cycloaddition reaction using an incorporated N-acetylglucosamine analog, and a strain-promoted azide-alkyne click reaction. All evaluated azido-monosaccharide analogs were observed to be non-toxic at the used concentrations under normal growth conditions. Our results for the metabolic incorporation and fluorescent labeling of these azido-monosaccharide analogs expand the possibilities for studying plant glycans by direct imaging. Overall we successfully evaluated five azido-monosaccharide analogs for their ability to be metabolically incorporated in Arabidopsis roots and their imaging after fluorescent labeling. This expands

21 CFR 862.2050 - General purpose laboratory equipment labeled or promoted for a specific medical use.

Code of Federal Regulations, 2012 CFR

2012-04-01

..., DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL... human body and that is labeled or promoted for a specific medical use. (b) Classification. Class I...

21 CFR 862.2050 - General purpose laboratory equipment labeled or promoted for a specific medical use.

Code of Federal Regulations, 2014 CFR

2014-04-01

..., DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL... human body and that is labeled or promoted for a specific medical use. (b) Classification. Class I...

21 CFR 862.2050 - General purpose laboratory equipment labeled or promoted for a specific medical use.

Code of Federal Regulations, 2013 CFR

2013-04-01

..., DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL... human body and that is labeled or promoted for a specific medical use. (b) Classification. Class I...

21 CFR 862.2050 - General purpose laboratory equipment labeled or promoted for a specific medical use.

Code of Federal Regulations, 2010 CFR

2010-04-01

... human body and that is labeled or promoted for a specific medical use. (b) Classification. Class I..., DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL...

21 CFR 862.2050 - General purpose laboratory equipment labeled or promoted for a specific medical use.

Code of Federal Regulations, 2011 CFR

2011-04-01

... human body and that is labeled or promoted for a specific medical use. (b) Classification. Class I..., DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES CLINICAL CHEMISTRY AND CLINICAL...

New horizons in cardiac innervation imaging: introduction of novel 18F-labeled PET tracers.

PubMed

Kobayashi, Ryohei; Chen, Xinyu; Werner, Rudolf A; Lapa, Constantin; Javadi, Mehrbod S; Higuchi, Takahiro

2017-12-01

Cardiac sympathetic nervous activity can be uniquely visualized by non-invasive radionuclide imaging techniques due to the fast growing and widespread application of nuclear cardiology in the last few years. The norepinephrine analogue 123 I-meta-iodobenzylguanidine ( 123 I-MIBG) is a single photon emission computed tomography (SPECT) tracer for the clinical implementation of sympathetic nervous imaging for both diagnosis and prognosis of heart failure. Meanwhile, positron emission tomography (PET) imaging has become increasingly attractive because of its higher spatial and temporal resolution compared to SPECT, which allows regional functional and dynamic kinetic analysis. Nevertheless, wider use of cardiac sympathetic nervous PET imaging is still limited mainly due to the demand of costly on-site cyclotrons, which are required for the production of conventional 11 C-labeled (radiological half-life, 20 min) PET tracers. Most recently, more promising 18 F-labeled (half-life, 110 min) PET radiopharmaceuticals targeting sympathetic nervous system have been introduced. These tracers optimize PET imaging and, by using delivery networks, cost less to produce. In this article, the latest advances of sympathetic nervous imaging using 18 F-labeled radiotracers along with their possible applications are reviewed.

Machine Learning Interface for Medical Image Analysis.

PubMed

Zhang, Yi C; Kagen, Alexander C

2017-10-01

TensorFlow is a second-generation open-source machine learning software library with a built-in framework for implementing neural networks in wide variety of perceptual tasks. Although TensorFlow usage is well established with computer vision datasets, the TensorFlow interface with DICOM formats for medical imaging remains to be established. Our goal is to extend the TensorFlow API to accept raw DICOM images as input; 1513 DaTscan DICOM images were obtained from the Parkinson's Progression Markers Initiative (PPMI) database. DICOM pixel intensities were extracted and shaped into tensors, or n-dimensional arrays, to populate the training, validation, and test input datasets for machine learning. A simple neural network was constructed in TensorFlow to classify images into normal or Parkinson's disease groups. Training was executed over 1000 iterations for each cross-validation set. The gradient descent optimization and Adagrad optimization algorithms were used to minimize cross-entropy between the predicted and ground-truth labels. Cross-validation was performed ten times to produce a mean accuracy of 0.938 ± 0.047 (95 % CI 0.908-0.967). The mean sensitivity was 0.974 ± 0.043 (95 % CI 0.947-1.00) and mean specificity was 0.822 ± 0.207 (95 % CI 0.694-0.950). We extended the TensorFlow API to enable DICOM compatibility in the context of DaTscan image analysis. We implemented a neural network classifier that produces diagnostic accuracies on par with excellent results from previous machine learning models. These results indicate the potential role of TensorFlow as a useful adjunct diagnostic tool in the clinical setting.

21 CFR 892.2010 - Medical image storage device.

Code of Federal Regulations, 2013 CFR

2013-04-01

... 21 Food and Drugs 8 2013-04-01 2013-04-01 false Medical image storage device. 892.2010 Section 892...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2010 Medical image storage device. (a) Identification. A medical image storage device is a device that provides electronic storage and retrieval...

21 CFR 892.2010 - Medical image storage device.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 21 Food and Drugs 8 2011-04-01 2011-04-01 false Medical image storage device. 892.2010 Section 892...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2010 Medical image storage device. (a) Identification. A medical image storage device is a device that provides electronic storage and retrieval...

21 CFR 892.2010 - Medical image storage device.

Code of Federal Regulations, 2014 CFR

2014-04-01

... 21 Food and Drugs 8 2014-04-01 2014-04-01 false Medical image storage device. 892.2010 Section 892...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2010 Medical image storage device. (a) Identification. A medical image storage device is a device that provides electronic storage and retrieval...

21 CFR 892.2010 - Medical image storage device.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Medical image storage device. 892.2010 Section 892...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2010 Medical image storage device. (a) Identification. A medical image storage device is a device that provides electronic storage and retrieval...

21 CFR 892.2010 - Medical image storage device.

Code of Federal Regulations, 2012 CFR

2012-04-01

... 21 Food and Drugs 8 2012-04-01 2012-04-01 false Medical image storage device. 892.2010 Section 892...) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2010 Medical image storage device. (a) Identification. A medical image storage device is a device that provides electronic storage and retrieval...

Medical Image Analysis by Cognitive Information Systems - a Review.

PubMed

Ogiela, Lidia; Takizawa, Makoto

2016-10-01

This publication presents a review of medical image analysis systems. The paradigms of cognitive information systems will be presented by examples of medical image analysis systems. The semantic processes present as it is applied to different types of medical images. Cognitive information systems were defined on the basis of methods for the semantic analysis and interpretation of information - medical images - applied to cognitive meaning of medical images contained in analyzed data sets. Semantic analysis was proposed to analyzed the meaning of data. Meaning is included in information, for example in medical images. Medical image analysis will be presented and discussed as they are applied to various types of medical images, presented selected human organs, with different pathologies. Those images were analyzed using different classes of cognitive information systems. Cognitive information systems dedicated to medical image analysis was also defined for the decision supporting tasks. This process is very important for example in diagnostic and therapy processes, in the selection of semantic aspects/features, from analyzed data sets. Those features allow to create a new way of analysis.

Label-free chemical imaging of live Euglena gracilis by high-speed SRS spectral microscopy (Conference Presentation)

NASA Astrophysics Data System (ADS)

Wakisaka, Yoshifumi; Suzuki, Yuta; Tokunaga, Kyoya; Hirose, Misa; Domon, Ryota; Akaho, Rina; Kuroshima, Mai; Tsumura, Norimichi; Shimobaba, Tomoyoshi; Iwata, Osamu; Suzuki, Kengo; Nakashima, Ayaka; Goda, Keisuke; Ozeki, Yasuyuki

2016-03-01

Microbes, especially microalgae, have recently been of great interest for developing novel biofuels, drugs, and biomaterials. Imaging-based screening of live cells can provide high selectivity and is attractive for efficient bio-production from microalgae. Although conventional cellular screening techniques use cell labeling, labeling of microbes is still under development and can interfere with their cellular functions. Furthermore, since live microbes move and change their shapes rapidly, a high-speed imaging technique is required to suppress motion artifacts. Stimulated Raman scattering (SRS) microscopy allows for label-free and high-speed spectral imaging, which helps us visualize chemical components inside biological cells and tissues. Here we demonstrate high-speed SRS imaging, with temporal resolution of 0.14 seconds, of intracellular distributions of lipid, polysaccharide, and chlorophyll concentrations in rapidly moving Euglena gracilis, a unicellular phytoflagellate. Furthermore, we show that our method allows us to analyze the amount of chemical components inside each living cell. Our results indicate that SRS imaging may be applied to label-free screening of living microbes based on chemical information.

Overview of deep learning in medical imaging.

PubMed

Suzuki, Kenji

2017-09-01

The use of machine learning (ML) has been increasing rapidly in the medical imaging field, including computer-aided diagnosis (CAD), radiomics, and medical image analysis. Recently, an ML area called deep learning emerged in the computer vision field and became very popular in many fields. It started from an event in late 2012, when a deep-learning approach based on a convolutional neural network (CNN) won an overwhelming victory in the best-known worldwide computer vision competition, ImageNet Classification. Since then, researchers in virtually all fields, including medical imaging, have started actively participating in the explosively growing field of deep learning. In this paper, the area of deep learning in medical imaging is overviewed, including (1) what was changed in machine learning before and after the introduction of deep learning, (2) what is the source of the power of deep learning, (3) two major deep-learning models: a massive-training artificial neural network (MTANN) and a convolutional neural network (CNN), (4) similarities and differences between the two models, and (5) their applications to medical imaging. This review shows that ML with feature input (or feature-based ML) was dominant before the introduction of deep learning, and that the major and essential difference between ML before and after deep learning is the learning of image data directly without object segmentation or feature extraction; thus, it is the source of the power of deep learning, although the depth of the model is an important attribute. The class of ML with image input (or image-based ML) including deep learning has a long history, but recently gained popularity due to the use of the new terminology, deep learning. There are two major models in this class of ML in medical imaging, MTANN and CNN, which have similarities as well as several differences. In our experience, MTANNs were substantially more efficient in their development, had a higher performance, and required a

Label-free optical imaging of lymphatic vessels within tissue beds in vivo

PubMed Central

Yousefi, Siavash; Zhi, Zhongwei; Wang, Ruikang K.

2015-01-01

Lymphatic vessels are a part of circulatory system in vertebrates that maintain tissue fluid homeostasis and drain excess fluid and large cells that cannot easily find their way back into venous system. Due to the lack of non-invasive monitoring tools, lymphatic vessels are known as forgotten circulation. However, lymphatic system plays an important role in diseases such as cancer and inflammatory conditions. In this paper, we start to briefly review the current existing methods for imaging lymphatic vessels, mostly involving dye/targeting cell injection. We then show the capability of optical coherence tomography (OCT) for label-free non-invasive in vivo imaging of lymph vessels and nodes. One of the advantages of using OCT over other imaging modalities is its ability to assess label-free blood flow perfusion that can be simultaneously observed along with lymphatic vessels for imaging the microcirculatory system within tissue beds. Imaging the microcirculatory system including blood and lymphatic vessels can be utilized for imaging and better understanding pathologic mechanisms and treatment technique development in some critical diseases such as inflammation, malignant cancer angiogenesis and metastasis. PMID:25642129

Novel Fitc-Labeled Igy Antibody: Fluorescence Imaging Toxoplasma Gondii In Vitro.

PubMed

Sert, Mehtap; Cakir Koc, Rabia; Budama Kilinc, Yasemin

2017-04-12

Toxoplasmosis is caused by T. gondii and can create serious health problems in humans and also worldwide economic harm. Because of the clinical and veterinary importance of toxoplasmosis, its timely and accurate diagnosis has a major impact on disease-fighting strategies. T. gondii surface antigen 1 (SAG1), an immunodominant-specific antigen, is often used as a diagnostic tool. Therefore, the aim of this study was the optimization of novel fluorescein isothiocyanate (FITC) labeling of the SAG1-specific IgY antibody to show the potential for immunofluorescence imaging of T. gondii in vitro. The specificity of IgY antibodies was controlled by an enzyme-linked immunosorbent assay (ELISA), and the concentration of the IgY antibody was detected using a spectrophotometer. The optimum incubation time and FITC concentration were determined with a fluorescence spectrometer. The obtained FITC-labeled IgY was used for marking T. gondii tachyzoites, which were cultured in vitro and viewed using light microscopy. The interaction of the fluorescence-labeled antibody and the T. gondii tachyzoites was examined with a fluorescence microscope. In this study, for the first time, a FITC-labeled anti-SAG1 IgY antibody was developed according to ELISA, fluorescence spectrometer, and fluorescence imaging of cell culture. In the future, the obtained FITC-labeled T. gondii tachyzoites' specific IgY antibodies may be used as diagnostic tools for the detection of T. gondii infections in different samples.

Medical image segmentation using genetic algorithms.

PubMed

Maulik, Ujjwal

2009-03-01

Genetic algorithms (GAs) have been found to be effective in the domain of medical image segmentation, since the problem can often be mapped to one of search in a complex and multimodal landscape. The challenges in medical image segmentation arise due to poor image contrast and artifacts that result in missing or diffuse organ/tissue boundaries. The resulting search space is therefore often noisy with a multitude of local optima. Not only does the genetic algorithmic framework prove to be effective in coming out of local optima, it also brings considerable flexibility into the segmentation procedure. In this paper, an attempt has been made to review the major applications of GAs to the domain of medical image segmentation.

Experimental basis of myocardial imaging with 123I-labeled hexadecenoic acid.

PubMed

Poe, N D; Robinson, G D; Graham, L S; MacDonald, N S

1976-12-01

Progress in myocardial perfusion imaging has been slowed by the lack or radiopharmaceuticals with suitable physical and biologic characteristics. Hexadecenoic acid, terminally labeled with 123I, partially overcomes these limitations by providing a compound that concentrates in the myocardium in proportion to relative regional blood flow and carries a gamma-emitter with desirable detection and imaging qualities. After intravenous injection in experimental animals, the clearance half-times of hexadecenoic acid for blood and myocardium are 1.7 and 20 min, respectively. These values compare favorably with 18-carbon fatty-acid analogs labeled with 11C. In acute and chronic infarction, similar distribution patterns are found for hexadecenoic acid and 43K, which indicates that hexadecenoic acid is a suitable substitute for the potassium analogs now in use for myocardial imaging. Because of the high count rates obtainable with 123I-hexadecenoic acid, good-guality images can be acquired in as little as 2-3 min per view. Iodine-123-hexadecenoic acid is potentially a useful radiopharmaceutical for clinical application.

Medical imaging, PACS, and imaging informatics: retrospective.

PubMed

Huang, H K

2014-01-01

Historical reviews of PACS (picture archiving and communication system) and imaging informatics development from different points of view have been published in the past (Huang in Euro J Radiol 78:163-176, 2011; Lemke in Euro J Radiol 78:177-183, 2011; Inamura and Jong in Euro J Radiol 78:184-189, 2011). This retrospective attempts to look at the topic from a different angle by identifying certain basic medical imaging inventions in the 1960s and 1970s which had conceptually defined basic components of PACS guiding its course of development in the 1980s and 1990s, as well as subsequent imaging informatics research in the 2000s. In medical imaging, the emphasis was on the innovations at Georgetown University in Washington, DC, in the 1960s and 1970s. During the 1980s and 1990s, research and training support from US government agencies and public and private medical imaging manufacturers became available for training of young talents in biomedical physics and for developing the key components required for PACS development. In the 2000s, computer hardware and software as well as communication networks advanced by leaps and bounds, opening the door for medical imaging informatics to flourish. Because many key components required for the PACS operation were developed by the UCLA PACS Team and its collaborative partners in the 1980s, this presentation is centered on that aspect. During this period, substantial collaborative research efforts by many individual teams in the US and in Japan were highlighted. Credits are due particularly to the Pattern Recognition Laboratory at Georgetown University, and the computed radiography (CR) development at the Fuji Electric Corp. in collaboration with Stanford University in the 1970s; the Image Processing Laboratory at UCLA in the 1980s-1990s; as well as the early PACS development at the Hokkaido University, Sapporo, Japan, in the late 1970s, and film scanner and digital radiography developed by Konishiroku Photo Ind. Co. Ltd

Viewpoints on Medical Image Processing: From Science to Application

PubMed Central

Deserno (né Lehmann), Thomas M.; Handels, Heinz; Maier-Hein (né Fritzsche), Klaus H.; Mersmann, Sven; Palm, Christoph; Tolxdorff, Thomas; Wagenknecht, Gudrun; Wittenberg, Thomas

2013-01-01

Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment. PMID:24078804

Viewpoints on Medical Image Processing: From Science to Application.

PubMed

Deserno Né Lehmann, Thomas M; Handels, Heinz; Maier-Hein Né Fritzsche, Klaus H; Mersmann, Sven; Palm, Christoph; Tolxdorff, Thomas; Wagenknecht, Gudrun; Wittenberg, Thomas

2013-05-01

Medical image processing provides core innovation for medical imaging. This paper is focused on recent developments from science to applications analyzing the past fifteen years of history of the proceedings of the German annual meeting on medical image processing (BVM). Furthermore, some members of the program committee present their personal points of views: (i) multi-modality for imaging and diagnosis, (ii) analysis of diffusion-weighted imaging, (iii) model-based image analysis, (iv) registration of section images, (v) from images to information in digital endoscopy, and (vi) virtual reality and robotics. Medical imaging and medical image computing is seen as field of rapid development with clear trends to integrated applications in diagnostics, treatment planning and treatment.

"Warning: This image has been digitally altered": The effect of disclaimer labels added to fashion magazine shoots on women's body dissatisfaction.

PubMed

Tiggemann, Marika; Brown, Zoe; Zaccardo, Mia; Thomas, Nicole

2017-06-01

The present experiment aimed to investigate the impact of the addition of disclaimer labels to fashion magazine shoots on women's body dissatisfaction. Participants were 320 female undergraduate students who viewed fashion shoots containing a thin and attractive model with no disclaimer label, or a small, large, or very large disclaimer label, or product images. Although thin-ideal fashion shoot images resulted in greater body dissatisfaction than product images, there was no significant effect of disclaimer label. Internalisation of the thin ideal was found to moderate the effect of disclaimer label, such that internalisation predicted increased body dissatisfaction in the no label and small label conditions, but not in the larger label conditions. Overall, the results showed no benefit for any size of disclaimer label in ameliorating the negative effect of viewing thin-ideal media images. It was concluded that more extensive research is required before the effective implementation of disclaimer labels. Copyright © 2017 Elsevier Ltd. All rights reserved.

Multimodality Molecular Imaging of [18F]-Fluorinated Carboplatin Derivative Encapsulated in [111In]-Labeled Liposomes

NASA Astrophysics Data System (ADS)

Lamichhane, Narottam

-(5-fluoro-pentyl)-2-methyl malonic acid as the labeling agent to coordinate with the cisplatin aqua complex. It was then used to treat various cell lines and compared with cisplatin and carboplatin at different concentrations ranging from 0.001 microM to 100 microM for 72 hrs and 96 hrs. IC50 values calculated from cell viability indicated that 19F-FCP is a more potent drug than Carboplatin. Manual radiosynthesis and characterization of [18F]-FCP was performed using [18F]-2-(5-fluoro-pentyl)-2-methyl malonic acid with coordination with cisplatin aqua complex. Automated radiosynthesis of [18F]-FCP was optimized using the manual synthetic procedures and using them as macros for the radiosynthesizer. [18F]-FCP was evaluated in vivo with detailed biodistribution studies and PET imaging in normal and KB 3-1 and KB 8-5 tumor xenograft bearing nude mice. The biodistribution studies and PET imaging of [18F]-FCP showed major uptake in kidneys which attributes to the renal clearance of radiotracer. In vivo plasma and urine stability demonstrated intact [18F]-FCP. [ 111In]-Labeled Liposomes was synthesized and physiochemical properties were assessed with DLS. [111In]-Labeled Liposome was evaluated in vivo with detailed pharmacokinetic studies and SPECT imaging. The biodistribution and ROI analysis from SPECT imaging showed the spleen and liver uptake of [111In]-Labeled Liposome and subsequent clearance of activity with time. [18F]-FCP encapsulated [111In]-Labeled Liposome was developed and physiochemical properties were characterized with DLS. [18F]-FCP encapsulated [111In]-Labeled Liposome was used for in vivo dual tracer PET and SPECT imaging from the same nanoconstruct in KB 3-1 (sensitive) and COLO 205 (resistant) tumor xenograft bearing nude mice. PET imaging of [18F]-FCP in KB 3-1 (sensitive) and COLO 205 (resistant) tumor xenograft bearing nude mice was performed. Naked [18F]-FCP and [18F]-FCP encapsulated [ 111In]-Labeled Liposome showed different pharmacokinetic profiles. PET

A study for watermark methods appropriate to medical images.

PubMed

Cho, Y; Ahn, B; Kim, J S; Kim, I Y; Kim, S I

2001-06-01

The network system, including the picture archiving and communication system (PACS), is essential in hospital and medical imaging fields these days. Many medical images are accessed and processed on the web, as well as in PACS. Therefore, any possible accidents caused by the illegal modification of medical images must be prevented. Digital image watermark techniques have been proposed as a method to protect against illegal copying or modification of copyrighted material. Invisible signatures made by a digital image watermarking technique can be a solution to these problems. However, medical images have some different characteristics from normal digital images in that one must not corrupt the information contained in the original medical images. In this study, we suggest modified watermark methods appropriate for medical image processing and communication system that prevent clinically important data contained in original images from being corrupted.

Large area, label-free imaging of extracellular matrix using telecentricity

NASA Astrophysics Data System (ADS)

Visbal Onufrak, Michelle A.; Konger, Raymond L.; Kim, Young L.

2017-02-01

Subtle alterations in stromal tissue structures and organizations within the extracellular matrix (ECM) have been observed in several types of tissue abnormalities, including early skin cancer and wounds. Current microscopic imaging methods often lack the ability to accurately determine the extent of malignancy over a large area, due to their limited field of view. In this research we focus on the development of simple mesoscopic (i.e. between microscopic and macroscopic) biomedical imaging device for non-invasive assessment of ECM alterations over a large, heterogeneous area. In our technology development, a telecentric lens, commonly used in machine vision systems but rarely used in biomedical imaging, serves as a key platform to visualize alterations in tissue microenvironments in a label-free manner over a clinically relevant area. In general, telecentric imaging represents a simple, alternative method for reducing unwanted scattering or diffuse light caused by the highly anisotropic scattering properties of biological tissue. In particular, under telecentric imaging the light intensity backscattered from biological tissue is mainly sensitive to the scattering anisotropy factor, possibly associated with the ECM. We demonstrate the inherent advantages of combining telecentric lens systems with hyperspectral imaging for providing optical information of tissue scattering in biological tissue of murine models, as well as light absorption of hemoglobin in blood vessel tissue phantoms. Thus, we envision that telecentric imaging could potentially serve for simple site-specific, tissue-based assessment of stromal alterations over a clinically relevant field of view in a label-free manner, for studying diseases associated with disruption of homeostasis in ECM.

Label-free imaging to study phenotypic behavioural traits of cells in complex co-cultures

NASA Astrophysics Data System (ADS)

Suman, Rakesh; Smith, Gabrielle; Hazel, Kathryn E. A.; Kasprowicz, Richard; Coles, Mark; O'Toole, Peter; Chawla, Sangeeta

2016-02-01

Time-lapse imaging is a fundamental tool for studying cellular behaviours, however studies of primary cells in complex co-culture environments often requires fluorescent labelling and significant light exposure that can perturb their natural function over time. Here, we describe ptychographic phase imaging that permits prolonged label-free time-lapse imaging of microglia in the presence of neurons and astrocytes, which better resembles in vivo microenvironments. We demonstrate the use of ptychography as an assay to study the phenotypic behaviour of microglial cells in primary neuronal co-cultures through the addition of cyclosporine A, a potent immune-modulator.

Glioblastoma cells labeled by robust Raman tags for enhancing imaging contrast.

PubMed

Huang, Li-Ching; Chang, Yung-Ching; Wu, Yi-Syuan; Sun, Wei-Lun; Liu, Chan-Chuan; Sze, Chun-I; Chen, Shiuan-Yeh

2018-05-01

Complete removal of a glioblastoma multiforme (GBM), a highly malignant brain tumor, is challenging due to its infiltrative characteristics. Therefore, utilizing imaging agents such as fluorophores to increase the contrast between GBM and normal cells can help neurosurgeons to locate residual cancer cells during image guided surgery. In this work, Raman tag based labeling and imaging for GBM cells in vitro is described and evaluated. The cell membrane of a GBM adsorbs a substantial amount of functionalized Raman tags through overexpression of the epidermal growth factor receptor (EGFR) and "broadcasts" stronger pre-defined Raman signals than normal cells. The average ratio between Raman signals from a GBM cell and autofluorescence from a normal cell can be up to 15. In addition, the intensity of these images is stable under laser illuminations without suffering from the severe photo-bleaching that usually occurs in fluorescent imaging. Our results show that labeling and imaging GBM cells via robust Raman tags is a viable alternative method to distinguish them from normal cells. This Raman tag based method can be used solely or integrated into an existing fluorescence system to improve the identification of infiltrative glial tumor cells around the boundary, which will further reduce GBM recurrence. In addition, it can also be applied/extended to other types of cancer to improve the effectiveness of image guided surgery.

Glioblastoma cells labeled by robust Raman tags for enhancing imaging contrast

PubMed Central

Huang, Li-Ching; Chang, Yung-Ching; Wu, Yi-Syuan; Sun, Wei-Lun; Liu, Chan-Chuan; Sze, Chun-I; Chen, Shiuan-Yeh

2018-01-01

Complete removal of a glioblastoma multiforme (GBM), a highly malignant brain tumor, is challenging due to its infiltrative characteristics. Therefore, utilizing imaging agents such as fluorophores to increase the contrast between GBM and normal cells can help neurosurgeons to locate residual cancer cells during image guided surgery. In this work, Raman tag based labeling and imaging for GBM cells in vitro is described and evaluated. The cell membrane of a GBM adsorbs a substantial amount of functionalized Raman tags through overexpression of the epidermal growth factor receptor (EGFR) and “broadcasts” stronger pre-defined Raman signals than normal cells. The average ratio between Raman signals from a GBM cell and autofluorescence from a normal cell can be up to 15. In addition, the intensity of these images is stable under laser illuminations without suffering from the severe photo-bleaching that usually occurs in fluorescent imaging. Our results show that labeling and imaging GBM cells via robust Raman tags is a viable alternative method to distinguish them from normal cells. This Raman tag based method can be used solely or integrated into an existing fluorescence system to improve the identification of infiltrative glial tumor cells around the boundary, which will further reduce GBM recurrence. In addition, it can also be applied/extended to other types of cancer to improve the effectiveness of image guided surgery. PMID:29760976

Imaging of experimental amyloidosis with /sup 131/I-labeled serum amyloid P component

DOE Office of Scientific and Technical Information (OSTI.GOV)

Caspi, D.; Zalzman, S.; Baratz, M.

1987-11-01

/sup 131/I-labeled human serum amyloid P component, which was injected into mice with experimentally induced systemic AA amyloidosis and into controls, became specifically localized and was retained in amyloidotic organs. In comparison, it was rapidly and completely eliminated from unaffected tissues and from control animals. Distinctive images of this amyloid-specific deposition of labeled serum amyloid P component were derived from whole body scanning, in vivo, of amyloidotic mice. These findings suggest that such imaging may have applications for the diagnosis and quantitation of amyloid deposits in humans.

Lossless medical image compression with a hybrid coder

NASA Astrophysics Data System (ADS)

Way, Jing-Dar; Cheng, Po-Yuen

1998-10-01

The volume of medical image data is expected to increase dramatically in the next decade due to the large use of radiological image for medical diagnosis. The economics of distributing the medical image dictate that data compression is essential. While there is lossy image compression, the medical image must be recorded and transmitted lossless before it reaches the users to avoid wrong diagnosis due to the image data lost. Therefore, a low complexity, high performance lossless compression schematic that can approach the theoretic bound and operate in near real-time is needed. In this paper, we propose a hybrid image coder to compress the digitized medical image without any data loss. The hybrid coder is constituted of two key components: an embedded wavelet coder and a lossless run-length coder. In this system, the medical image is compressed with the lossy wavelet coder first, and the residual image between the original and the compressed ones is further compressed with the run-length coder. Several optimization schemes have been used in these coders to increase the coding performance. It is shown that the proposed algorithm is with higher compression ratio than run-length entropy coders such as arithmetic, Huffman and Lempel-Ziv coders.

Crypto-Watermarking of Transmitted Medical Images.

PubMed

Al-Haj, Ali; Mohammad, Ahmad; Amer, Alaa'

2017-02-01

Telemedicine is a booming healthcare practice that has facilitated the exchange of medical data and expertise between healthcare entities. However, the widespread use of telemedicine applications requires a secured scheme to guarantee confidentiality and verify authenticity and integrity of exchanged medical data. In this paper, we describe a region-based, crypto-watermarking algorithm capable of providing confidentiality, authenticity, and integrity for medical images of different modalities. The proposed algorithm provides authenticity by embedding robust watermarks in images' region of non-interest using SVD in the DWT domain. Integrity is provided in two levels: strict integrity implemented by a cryptographic hash watermark, and content-based integrity implemented by a symmetric encryption-based tamper localization scheme. Confidentiality is achieved as a byproduct of hiding patient's data in the image. Performance of the algorithm was evaluated with respect to imperceptibility, robustness, capacity, and tamper localization, using different medical images. The results showed the effectiveness of the algorithm in providing security for telemedicine applications.

Traversing and labeling interconnected vascular tree structures from 3D medical images

NASA Astrophysics Data System (ADS)

O'Dell, Walter G.; Govindarajan, Sindhuja Tirumalai; Salgia, Ankit; Hegde, Satyanarayan; Prabhakaran, Sreekala; Finol, Ender A.; White, R. James

2014-03-01

Purpose: Detailed characterization of pulmonary vascular anatomy has important applications for the diagnosis and management of a variety of vascular diseases. Prior efforts have emphasized using vessel segmentation to gather information on the number or branches, number of bifurcations, and branch length and volume, but accurate traversal of the vessel tree to identify and repair erroneous interconnections between adjacent branches and neighboring tree structures has not been carefully considered. In this study, we endeavor to develop and implement a successful approach to distinguishing and characterizing individual vascular trees from among a complex intermingling of trees. Methods: We developed strategies and parameters in which the algorithm identifies and repairs false branch inter-tree and intra-tree connections to traverse complicated vessel trees. A series of two-dimensional (2D) virtual datasets with a variety of interconnections were constructed for development, testing, and validation. To demonstrate the approach, a series of real 3D computed tomography (CT) lung datasets were obtained, including that of an anthropomorphic chest phantom; an adult human chest CT; a pediatric patient chest CT; and a micro-CT of an excised rat lung preparation. Results: Our method was correct in all 2D virtual test datasets. For each real 3D CT dataset, the resulting simulated vessel tree structures faithfully depicted the vessel tree structures that were originally extracted from the corresponding lung CT scans. Conclusion: We have developed a comprehensive strategy for traversing and labeling interconnected vascular trees and successfully implemented its application to pulmonary vessels observed using 3D CT images of the chest.

Neuronal Tracing with Magnetic Labels: NMR Imaging Methods, Preliminary Results, and New Optimized Coils.

NASA Astrophysics Data System (ADS)

Ghosh, Pratik

1992-01-01

The investigations focussed on in vivo NMR imaging studies of magnetic particles with and within neural cells. NMR imaging methods, both Fourier transform and projection reconstruction, were implemented and new protocols were developed to perform "Neuronal Tracing with Magnetic Labels" on small animal brains. Having performed the preliminary experiments with neuronal tracing, new optimized coils and experimental set-up were devised. A novel gradient coil technology along with new rf-coils were implemented, and optimized for future use with small animals in them. A new magnetic labelling procedure was developed that allowed labelling of billions of cells with ultra -small magnetite particles in a short time. The relationships among the viability of such cells, the amount of label and the contrast in the images were studied as quantitatively as possible. Intracerebral grafting of magnetite labelled fetal rat brain cells made it possible for the first time to attempt monitoring in vivo the survival, differentiation, and possible migration of both host and grafted cells in the host rat brain. This constituted the early steps toward future experiments that may lead to the monitoring of human brain grafts of fetal brain cells. Preliminary experiments with direct injection of horse radish peroxidase-conjugated magnetite particles into neurons, followed by NMR imaging, revealed a possible non-invasive alternative, allowing serial study of the dynamic transport pattern of tracers in single living animals. New gradient coils were built by using parallel solid-conductor ribbon cables that could be wrapped easily and quickly. Rapid rise times provided by these coils allowed implementation of fast imaging methods. Optimized rf-coil circuit development made it possible to understand better the sample-coil properties and the associated trade -offs in cases of small but conducting samples.

Medical image informatics infrastructure design and applications.

PubMed

Huang, H K; Wong, S T; Pietka, E

1997-01-01

Picture archiving and communication systems (PACS) is a system integration of multimodality images and health information systems designed for improving the operation of a radiology department. As it evolves, PACS becomes a hospital image document management system with a voluminous image and related data file repository. A medical image informatics infrastructure can be designed to take advantage of existing data, providing PACS with add-on value for health care service, research, and education. A medical image informatics infrastructure (MIII) consists of the following components: medical images and associated data (including PACS database), image processing, data/knowledge base management, visualization, graphic user interface, communication networking, and application oriented software. This paper describes these components and their logical connection, and illustrates some applications based on the concept of the MIII.

Deep Learning in Medical Image Analysis

PubMed Central

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2016-01-01

The computer-assisted analysis for better interpreting images have been longstanding issues in the medical imaging field. On the image-understanding front, recent advances in machine learning, especially, in the way of deep learning, have made a big leap to help identify, classify, and quantify patterns in medical images. Specifically, exploiting hierarchical feature representations learned solely from data, instead of handcrafted features mostly designed based on domain-specific knowledge, lies at the core of the advances. In that way, deep learning is rapidly proving to be the state-of-the-art foundation, achieving enhanced performances in various medical applications. In this article, we introduce the fundamentals of deep learning methods; review their successes to image registration, anatomical/cell structures detection, tissue segmentation, computer-aided disease diagnosis or prognosis, and so on. We conclude by raising research issues and suggesting future directions for further improvements. PMID:28301734

Desktop publishing and medical imaging: paper as hardcopy medium for digital images.

PubMed

Denslow, S

1994-08-01

Desktop-publishing software and hardware has progressed to the point that many widely used word-processing programs are capable of printing high-quality digital images with many shades of gray from black to white. Accordingly, it should be relatively easy to print digital medical images on paper for reports, instructional materials, and in research notes. Components were assembled that were necessary for extracting image data from medical imaging devices and converting the data to a form usable by word-processing software. A system incorporating these components was implemented in a medical setting and has been operating for 18 months. The use of this system by medical staff has been monitored.

Deep Learning in Medical Image Analysis.

PubMed

Shen, Dinggang; Wu, Guorong; Suk, Heung-Il

2017-06-21

This review covers computer-assisted analysis of images in the field of medical imaging. Recent advances in machine learning, especially with regard to deep learning, are helping to identify, classify, and quantify patterns in medical images. At the core of these advances is the ability to exploit hierarchical feature representations learned solely from data, instead of features designed by hand according to domain-specific knowledge. Deep learning is rapidly becoming the state of the art, leading to enhanced performance in various medical applications. We introduce the fundamentals of deep learning methods and review their successes in image registration, detection of anatomical and cellular structures, tissue segmentation, computer-aided disease diagnosis and prognosis, and so on. We conclude by discussing research issues and suggesting future directions for further improvement.

The Orthanc Ecosystem for Medical Imaging.

PubMed

Jodogne, Sébastien

2018-05-03

This paper reviews the components of Orthanc, a free and open-source, highly versatile ecosystem for medical imaging. At the core of the Orthanc ecosystem, the Orthanc server is a lightweight vendor neutral archive that provides PACS managers with a powerful environment to automate and optimize the imaging flows that are very specific to each hospital. The Orthanc server can be extended with plugins that provide solutions for teleradiology, digital pathology, or enterprise-ready databases. It is shown how software developers and research engineers can easily develop external software or Web portals dealing with medical images, with minimal knowledge of the DICOM standard, thanks to the advanced programming interface of the Orthanc server. The paper concludes by introducing the Stone of Orthanc, an innovative toolkit for the cross-platform rendering of medical images.

Fluorine-19 Labeling of Stromal Vascular Fraction Cells for Clinical Imaging Applications

PubMed Central

Rose, Laura C.; Kadayakkara, Deepak K.; Wang, Guan; Bar-Shir, Amnon; Helfer, Brooke M.; O’Hanlon, Charles F.; Kraitchman, Dara L.; Rodriguez, Ricardo L.

2015-01-01

Stromal vascular fraction (SVF) cells are used clinically for various therapeutic targets. The location and persistence of engrafted SVF cells are important parameters for determining treatment failure versus success. We used the GID SVF-1 platform and a clinical protocol to harvest and label SVF cells with the fluorinated (19F) agent CS-1000 as part of a first-in-human phase I trial (clinicaltrials.gov identifier NCT02035085) to track SVF cells with magnetic resonance imaging during treatment of radiation-induced fibrosis in breast cancer patients. Flow cytometry revealed that SVF cells consisted of 25.0% ± 15.8% CD45+, 24.6% ± 12.5% CD34+, and 7.5% ± 3.3% CD31+ cells, with 2.1 ± 0.7 × 105 cells per cubic centimeter of adipose tissue obtained. Fluorescent CS-1000 (CS-ATM DM Green) labeled 87.0% ± 13.5% of CD34+ progenitor cells compared with 47.8% ± 18.5% of hematopoietic CD45+ cells, with an average of 2.8 ± 2.0 × 1012 19F atoms per cell, determined using nuclear magnetic resonance spectroscopy. The vast majority (92.7% ± 5.0%) of CD31+ cells were also labeled, although most coexpressed CD34. Only 16% ± 22.3% of CD45−/CD31−/CD34− (triple-negative) cells were labeled with CS-ATM DM Green. After induction of cell death by either apoptosis or necrosis, >95% of 19F was released from the cells, indicating that fluorine retention can be used as a surrogate marker for cell survival. Labeled-SVF cells engrafted in a silicone breast phantom could be visualized with a clinical 3-Tesla magnetic resonance imaging scanner at a sensitivity of approximately 2 × 106 cells at a depth of 5 mm. The current protocol can be used to image transplanted SVF cells at clinically relevant cell concentrations in patients. Significance Stromal vascular fraction (SVF) cells harvested from adipose tissue offer great promise in regenerative medicine, but methods to track such cell therapies are needed to ensure correct administration and monitor survival. A clinical protocol

A hierarchical SVG image abstraction layer for medical imaging

NASA Astrophysics Data System (ADS)

Kim, Edward; Huang, Xiaolei; Tan, Gang; Long, L. Rodney; Antani, Sameer

2010-03-01

As medical imaging rapidly expands, there is an increasing need to structure and organize image data for efficient analysis, storage and retrieval. In response, a large fraction of research in the areas of content-based image retrieval (CBIR) and picture archiving and communication systems (PACS) has focused on structuring information to bridge the "semantic gap", a disparity between machine and human image understanding. An additional consideration in medical images is the organization and integration of clinical diagnostic information. As a step towards bridging the semantic gap, we design and implement a hierarchical image abstraction layer using an XML based language, Scalable Vector Graphics (SVG). Our method encodes features from the raw image and clinical information into an extensible "layer" that can be stored in a SVG document and efficiently searched. Any feature extracted from the raw image including, color, texture, orientation, size, neighbor information, etc., can be combined in our abstraction with high level descriptions or classifications. And our representation can natively characterize an image in a hierarchical tree structure to support multiple levels of segmentation. Furthermore, being a world wide web consortium (W3C) standard, SVG is able to be displayed by most web browsers, interacted with by ECMAScript (standardized scripting language, e.g. JavaScript, JScript), and indexed and retrieved by XML databases and XQuery. Using these open source technologies enables straightforward integration into existing systems. From our results, we show that the flexibility and extensibility of our abstraction facilitates effective storage and retrieval of medical images.

Multi-atlas based segmentation using probabilistic label fusion with adaptive weighting of image similarity measures.

PubMed

Sjöberg, C; Ahnesjö, A

2013-06-01

Label fusion multi-atlas approaches for image segmentation can give better segmentation results than single atlas methods. We present a multi-atlas label fusion strategy based on probabilistic weighting of distance maps. Relationships between image similarities and segmentation similarities are estimated in a learning phase and used to derive fusion weights that are proportional to the probability for each atlas to improve the segmentation result. The method was tested using a leave-one-out strategy on a database of 21 pre-segmented prostate patients for different image registrations combined with different image similarity scorings. The probabilistic weighting yields results that are equal or better compared to both fusion with equal weights and results using the STAPLE algorithm. Results from the experiments demonstrate that label fusion by weighted distance maps is feasible, and that probabilistic weighted fusion improves segmentation quality more the stronger the individual atlas segmentation quality depends on the corresponding registered image similarity. The regions used for evaluation of the image similarity measures were found to be more important than the choice of similarity measure. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

High-efficiency dual labeling of influenza virus for single-virus imaging.

PubMed

Liu, Shu-Lin; Tian, Zhi-Quan; Zhang, Zhi-Ling; Wu, Qiu-Mei; Zhao, Hai-Su; Ren, Bin; Pang, Dai-Wen

2012-11-01

Many viruses invade host cells by entering the cells and releasing their genome for replication, which are remarkable incidents for viral infection. Therefore, the viral internal and external components should be simultaneously labeled and dynamically tracked at single-virus level for further understanding viral infection mechanisms. However, most of the previously reported methods have very low labeling efficiency and require considerable time and effort, which is laborious and inconvenient for researchers. In this work, we report a general strategy to high-efficiently label viral envelope and genome for single-virus imaging with quantum dots (QDs) and Syto 82, respectively. It was found that nearly all viral envelopes could be labeled with QDs with superior stability, which makes it possible to realize global and long-term tracking of single virus in individual cells. Effectively labeling their genome with Syto 82, about 90% of QDs-labeled viruses could be used to monitor the viral genome signal, which may provide valuable information for deeply studying viral genome transport. This is very important and meaningful to investigate the viral infection mechanism. Our labeling strategy has advantage in commonality, convenience and efficiency, which is expected to be widely used in biological research. Copyright © 2012 Elsevier Ltd. All rights reserved.

Whole-organ atlas imaged by label-free high-resolution photoacoustic microscopy assisted by a microtome

NASA Astrophysics Data System (ADS)

Wong, Terence T. W.; Zhang, Ruiying; Hsu, Hsun-Chia; Maslov, Konstantin I.; Shi, Junhui; Chen, Ruimin; Shung, K. Kirk; Zhou, Qifa; Wang, Lihong V.

2018-02-01

In biomedical imaging, all optical techniques face a fundamental trade-off between spatial resolution and tissue penetration. Therefore, obtaining an organelle-level resolution image of a whole organ has remained a challenging and yet appealing scientific pursuit. Over the past decade, optical microscopy assisted by mechanical sectioning or chemical clearing of tissue has been demonstrated as a powerful technique to overcome this dilemma, one of particular use in imaging the neural network. However, this type of techniques needs lengthy special preparation of the tissue specimen, which hinders broad application in life sciences. Here, we propose a new label-free three-dimensional imaging technique, named microtomy-assisted photoacoustic microscopy (mPAM), for potentially imaging all biomolecules with 100% endogenous natural staining in whole organs with high fidelity. We demonstrate the first label-free mPAM, using UV light for label-free histology-like imaging, in whole organs (e.g., mouse brains), most of them formalin-fixed and paraffin- or agarose-embedded for minimal morphological deformation. Furthermore, mPAM with dual wavelength illuminations is also employed to image a mouse brain slice, demonstrating the potential for imaging of multiple biomolecules without staining. With visible light illumination, mPAM also shows its deep tissue imaging capability, which enables less slicing and hence reduces sectioning artifacts. mPAM could potentially provide a new insight for understanding complex biological organs.

Automatic medical image annotation and keyword-based image retrieval using relevance feedback.

PubMed

Ko, Byoung Chul; Lee, JiHyeon; Nam, Jae-Yeal

2012-08-01

This paper presents novel multiple keywords annotation for medical images, keyword-based medical image retrieval, and relevance feedback method for image retrieval for enhancing image retrieval performance. For semantic keyword annotation, this study proposes a novel medical image classification method combining local wavelet-based center symmetric-local binary patterns with random forests. For keyword-based image retrieval, our retrieval system use the confidence score that is assigned to each annotated keyword by combining probabilities of random forests with predefined body relation graph. To overcome the limitation of keyword-based image retrieval, we combine our image retrieval system with relevance feedback mechanism based on visual feature and pattern classifier. Compared with other annotation and relevance feedback algorithms, the proposed method shows both improved annotation performance and accurate retrieval results.

Label-free optical imaging of nonfluorescent molecules by stimulated radiation.

PubMed

Min, Wei

2011-12-01

Imaging contrasts other than fluorescence are highly desirable for label-free detection and interrogation of nonfluorescent molecular species inside live cells, tissues, and organisms. The recently developed stimulated Raman scattering (SRS) and stimulated emission microscopy techniques provide sensitive and specific contrast mechanisms for nonfluorescent species, by employing the light amplification aspect of stimulated radiation. Compared to their spontaneous counterparts, stimulated radiation can enhance the imaging performance significantly, making the previously 'dark' molecules observable. Here we review and summarize the underlying principles of this emerging class of molecular imaging techniques. Copyright © 2011 Elsevier Ltd. All rights reserved.

Label-free imaging of developing vasculature in zebrafish with phase variance optical coherence microscopy

NASA Astrophysics Data System (ADS)

Chen, Yu; Fingler, Jeff; Trinh, Le A.; Fraser, Scott E.

2016-03-01

A phase variance optical coherence microscope (pvOCM) has been created to visualize blood flow in the vasculature of zebrafish embryos, without using exogenous labels. The pvOCM imaging system has axial and lateral resolutions of 2 μm in tissue, and imaging depth of more than 100 μm. Imaging of 2-5 days post-fertilization zebrafish embryos identified the detailed structures of somites, spinal cord, gut and notochord based on intensity contrast. Visualization of the blood flow in the aorta, veins and intersegmental vessels was achieved with phase variance contrast. The pvOCM vasculature images were confirmed with corresponding fluorescence microscopy of a zebrafish transgene that labels the vasculature with green fluorescent protein. The pvOCM images also revealed functional information of the blood flow activities that is crucial for the study of vascular development.

Label-free and live cell imaging by interferometric scattering microscopy.

PubMed

Park, Jin-Sung; Lee, Il-Buem; Moon, Hyeon-Min; Joo, Jong-Hyeon; Kim, Kyoung-Hoon; Hong, Seok-Cheol; Cho, Minhaeng

2018-03-14

Despite recent remarkable advances in microscopic techniques, it still remains very challenging to directly observe the complex structure of cytoplasmic organelles in live cells without a fluorescent label. Here we report label-free and live-cell imaging of mammalian cell, Escherischia coli , and yeast, using interferometric scattering microscopy, which reveals the underlying structures of a variety of cytoplasmic organelles as well as the underside structure of the cells. The contact areas of the cells attached onto a glass substrate, e.g. , focal adhesions and filopodia, are clearly discernible. We also found a variety of fringe-like features in the cytoplasmic area, which may reflect the folded structures of cytoplasmic organelles. We thus anticipate that the label-free interferometric scattering microscopy can be used as a powerful tool to shed interferometric light on in vivo structures and dynamics of various intracellular phenomena.

The semiotics of medical image Segmentation.

PubMed

Baxter, John S H; Gibson, Eli; Eagleson, Roy; Peters, Terry M

2018-02-01

As the interaction between clinicians and computational processes increases in complexity, more nuanced mechanisms are required to describe how their communication is mediated. Medical image segmentation in particular affords a large number of distinct loci for interaction which can act on a deep, knowledge-driven level which complicates the naive interpretation of the computer as a symbol processing machine. Using the perspective of the computer as dialogue partner, we can motivate the semiotic understanding of medical image segmentation. Taking advantage of Peircean semiotic traditions and new philosophical inquiry into the structure and quality of metaphors, we can construct a unified framework for the interpretation of medical image segmentation as a sign exchange in which each sign acts as an interface metaphor. This allows for a notion of finite semiosis, described through a schematic medium, that can rigorously describe how clinicians and computers interpret the signs mediating their interaction. Altogether, this framework provides a unified approach to the understanding and development of medical image segmentation interfaces. Copyright © 2017 Elsevier B.V. All rights reserved.

A survey of GPU-based medical image computing techniques

PubMed Central

Shi, Lin; Liu, Wen; Zhang, Heye; Xie, Yongming

2012-01-01

Medical imaging currently plays a crucial role throughout the entire clinical applications from medical scientific research to diagnostics and treatment planning. However, medical imaging procedures are often computationally demanding due to the large three-dimensional (3D) medical datasets to process in practical clinical applications. With the rapidly enhancing performances of graphics processors, improved programming support, and excellent price-to-performance ratio, the graphics processing unit (GPU) has emerged as a competitive parallel computing platform for computationally expensive and demanding tasks in a wide range of medical image applications. The major purpose of this survey is to provide a comprehensive reference source for the starters or researchers involved in GPU-based medical image processing. Within this survey, the continuous advancement of GPU computing is reviewed and the existing traditional applications in three areas of medical image processing, namely, segmentation, registration and visualization, are surveyed. The potential advantages and associated challenges of current GPU-based medical imaging are also discussed to inspire future applications in medicine. PMID:23256080

Off-label prescribing of medications for pain: maintaining optimal care at an intersection of law, public policy, and ethics.

PubMed

Ruble, James

2012-06-01

For more than 60 years, regulations limited marketing of medications for off-label uses to very low levels. Some key policy changes in the late 1990s ushered in an era of deregulation of off-label marketing. Policy changes included revised United States federal law as well as modifications of Food and Drug Administration (FDA) regulations. Subsequent investigations documented an explosion in scope off-label prescribing. Attempts to limit off-label advertising by manufacturers were vigorously challenged in the courts. Other modalities are needed to maintain a clinical care environment that places the patients' best interests first. In many circumstances, an off-label medication may be in the patient's best interests; however, where there is a lower level of clinical justification, the informed consent of the patient and shared decision making of the patient is essential to optimize outcome.

Label-Free Raman Imaging to Monitor Breast Tumor Signatures.

PubMed

Manciu, Felicia S; Ciubuc, John D; Parra, Karla; Manciu, Marian; Bennet, Kevin E; Valenzuela, Paloma; Sundin, Emma M; Durrer, William G; Reza, Luis; Francia, Giulio

2017-08-01

Although not yet ready for clinical application, methods based on Raman spectroscopy have shown significant potential in identifying, characterizing, and discriminating between noncancerous and cancerous specimens. Real-time and accurate medical diagnosis achievable through this vibrational optical method largely benefits from improvements in current technological and software capabilities. Not only is the acquisition of spectral information now possible in milliseconds and analysis of hundreds of thousands of data points achieved in minutes, but Raman spectroscopy also allows simultaneous detection and monitoring of several biological components. Besides demonstrating a significant Raman signature distinction between nontumorigenic (MCF-10A) and tumorigenic (MCF-7) breast epithelial cells, our study demonstrates that Raman can be used as a label-free method to evaluate epidermal growth factor activity in tumor cells. Comparative Raman profiles and images of specimens in the presence or absence of epidermal growth factor show important differences in regions attributed to lipid, protein, and nucleic acid vibrations. The occurrence, which is dependent on the presence of epidermal growth factor, of new Raman features associated with the appearance of phosphothreonine and phosphoserine residues reflects a signal transduction from the membrane to the nucleus, with concomitant modification of DNA/RNA structural characteristics. Parallel Western blotting analysis reveals an epidermal growth factor induction of phosphorylated Akt protein, corroborating the Raman results. The analysis presented in this work is an important step toward Raman-based evaluation of biological activity of epidermal growth factor receptors on the surfaces of breast cancer cells. With the ultimate future goal of clinically implementing Raman-guided techniques for the diagnosis of breast tumors (e.g., with regard to specific receptor activity), the current results just lay the foundation for

Direct fluorescent-dye labeling of α-tubulin in mammalian cells for live cell and superresolution imaging

PubMed Central

Schvartz, Tomer; Aloush, Noa; Goliand, Inna; Segal, Inbar; Nachmias, Dikla; Arbely, Eyal; Elia, Natalie

2017-01-01

Genetic code expansion and bioorthogonal labeling provide for the first time a way for direct, site-specific labeling of proteins with fluorescent-dyes in live cells. Although the small size and superb photophysical parameters of fluorescent-dyes offer unique advantages for high-resolution microscopy, this approach has yet to be embraced as a tool in live cell imaging. Here we evaluated the feasibility of this approach by applying it for α-tubulin labeling. After a series of calibrations, we site-specifically labeled α-tubulin with silicon rhodamine (SiR) in live mammalian cells in an efficient and robust manner. SiR-labeled tubulin successfully incorporated into endogenous microtubules at high density, enabling video recording of microtubule dynamics in interphase and mitotic cells. Applying this labeling approach to structured illumination microscopy resulted in an increase in resolution, highlighting the advantages in using a smaller, brighter tag. Therefore, using our optimized assay, genetic code expansion provides an attractive tool for labeling proteins with a minimal, bright tag in quantitative high-resolution imaging. PMID:28835375

Medical imaging systems

DOEpatents

Frangioni, John V [Wayland, MA

2012-07-24

A medical imaging system provides simultaneous rendering of visible light and fluorescent images. The system may employ dyes in a small-molecule form that remains in a subject's blood stream for several minutes, allowing real-time imaging of the subject's circulatory system superimposed upon a conventional, visible light image of the subject. The system may also employ dyes or other fluorescent substances associated with antibodies, antibody fragments, or ligands that accumulate within a region of diagnostic significance. In one embodiment, the system provides an excitation light source to excite the fluorescent substance and a visible light source for general illumination within the same optical guide that is used to capture images. In another embodiment, the system is configured for use in open surgical procedures by providing an operating area that is closed to ambient light. More broadly, the systems described herein may be used in imaging applications where a visible light image may be usefully supplemented by an image formed from fluorescent emissions from a fluorescent substance that marks areas of functional interest.

A digital library for medical imaging activities

NASA Astrophysics Data System (ADS)

dos Santos, Marcelo; Furuie, Sérgio S.

2007-03-01

This work presents the development of an electronic infrastructure to make available a free, online, multipurpose and multimodality medical image database. The proposed infrastructure implements a distributed architecture for medical image database, authoring tools, and a repository for multimedia documents. Also it includes a peer-reviewed model that assures quality of dataset. This public repository provides a single point of access for medical images and related information to facilitate retrieval tasks. The proposed approach has been used as an electronic teaching system in Radiology as well.

A boosting framework for visuality-preserving distance metric learning and its application to medical image retrieval.

PubMed

Yang, Liu; Jin, Rong; Mummert, Lily; Sukthankar, Rahul; Goode, Adam; Zheng, Bin; Hoi, Steven C H; Satyanarayanan, Mahadev

2010-01-01

Similarity measurement is a critical component in content-based image retrieval systems, and learning a good distance metric can significantly improve retrieval performance. However, despite extensive study, there are several major shortcomings with the existing approaches for distance metric learning that can significantly affect their application to medical image retrieval. In particular, "similarity" can mean very different things in image retrieval: resemblance in visual appearance (e.g., two images that look like one another) or similarity in semantic annotation (e.g., two images of tumors that look quite different yet are both malignant). Current approaches for distance metric learning typically address only one goal without consideration of the other. This is problematic for medical image retrieval where the goal is to assist doctors in decision making. In these applications, given a query image, the goal is to retrieve similar images from a reference library whose semantic annotations could provide the medical professional with greater insight into the possible interpretations of the query image. If the system were to retrieve images that did not look like the query, then users would be less likely to trust the system; on the other hand, retrieving images that appear superficially similar to the query but are semantically unrelated is undesirable because that could lead users toward an incorrect diagnosis. Hence, learning a distance metric that preserves both visual resemblance and semantic similarity is important. We emphasize that, although our study is focused on medical image retrieval, the problem addressed in this work is critical to many image retrieval systems. We present a boosting framework for distance metric learning that aims to preserve both visual and semantic similarities. The boosting framework first learns a binary representation using side information, in the form of labeled pairs, and then computes the distance as a weighted Hamming

A dual-labeled knottin peptide for PET and near-infrared fluorescence imaging of integrin expression in living subjects

PubMed Central

Kimura, Richard H.; Miao, Zheng; Cheng, Zhen; Gambhir, Sanjiv S.; Cochran, Jennifer R.

2010-01-01

Previously, we used directed evolution to engineer mutants of the Ecballium elaterium trypsin inhibitor (EETI-II) knottin that bind to αvβ3 and αvβ5 integrin receptors with low nanomolar affinity, and showed that Cy5.5- or 64Cu-DOTA-labeled knottin peptides could be used to image integrin expression in mouse tumor models using near-infrared fluorescence (NIRF) imaging or positron emission tomography (PET). Here, we report the development of a dual-labeled knottin peptide conjugated to both NIRF and PET imaging agents for multimodality imaging in living subjects. We created an orthogonally-protected peptide-based linker for stoichiometric coupling of 64Cu-DOTA and Cy5.5 onto the knottin N-terminus, and confirmed that conjugation did not affect binding to αvβ3 and αvβ5 integrins. NIRF and PET imaging studies in tumor xenograft models showed that Cy5.5 conjugation significantly increased kidney uptake and retention compared to the knottin peptide labeled with 64Cu-DOTA alone. In the tumor, the dual-labeled 64Cu-DOTA/Cy5.5 knottin probe showed decreased wash-out leading to significantly better retention (p < 0.05) compared to the 64Cu-DOTA-labeled knottin probe. Tumor uptake was significantly reduced (p < 0.05) when the dual-labeled probe was co-injected with an excess of unlabeled competitor and when tested in a tumor model with lower levels of integrin expression. Finally, plots of tumor-to-background tissue ratios for Cy5.5 versus 64Cu uptake were well correlated over several time points post injection, demonstrating pharmacokinetic cross validation of imaging labels. This dual-modality NIRF/PET imaging agent is promising for further development in clinical applications where high sensitivity and high-resolution are desired, such as detection of tumors located deep within the body and image-guided surgical resection. PMID:20131753

A RONI Based Visible Watermarking Approach for Medical Image Authentication.

PubMed

Thanki, Rohit; Borra, Surekha; Dwivedi, Vedvyas; Borisagar, Komal

2017-08-09

Nowadays medical data in terms of image files are often exchanged between different hospitals for use in telemedicine and diagnosis. Visible watermarking being extensively used for Intellectual Property identification of such medical images, leads to serious issues if failed to identify proper regions for watermark insertion. In this paper, the Region of Non-Interest (RONI) based visible watermarking for medical image authentication is proposed. In this technique, to RONI of the cover medical image is first identified using Human Visual System (HVS) model. Later, watermark logo is visibly inserted into RONI of the cover medical image to get watermarked medical image. Finally, the watermarked medical image is compared with the original medical image for measurement of imperceptibility and authenticity of proposed scheme. The experimental results showed that this proposed scheme reduces the computational complexity and improves the PSNR when compared to many existing schemes.

Subcellular SIMS imaging of isotopically labeled amino acids in cryogenically prepared cells

NASA Astrophysics Data System (ADS)

Chandra, Subhash

2004-06-01

Ion microscopy is a potentially powerful technique for localization of isotopically labeled molecules. In this study, L-arginine and phenylalanine amino acids labeled with stable isotopes 13C and 15N were localized in cultured cells with the ion microscope at 500 nm spatial resolution. Cells were exposed to the labeled amino acids and cryogenically prepared. SIMS analyses were made in fractured freeze-dried cells. A dynamic distribution was observed from labeled arginine-treated LLC-PK 1 kidney cells at mass 28 ( 13C15N) in negative secondaries, revealing cell-to-cell heterogeneity and preferential accumulation of the amino acid (or its metabolite) in the nucleus and nucleolus of some cells. The smaller nucleolus inside the nucleus was clearly resolved in SIMS images and confirmed by correlative light microscopy. The distribution of labeled phenylalanine contrasted with arginine as it was rather homogeneously distributed in T98G human glioblastoma cells. Images of 39K, 23Na and 40Ca were also recorded to confirm the reliability of sample preparation and authenticity of the observed amino acid distributions. These observations indicate that SIMS techniques can provide a valuable technology for subcellular localization of nitrogen-containing molecules in proteomics since nitrogen does not have a radionuclide tracer isotope. Amino acids labeled with stable isotopes can be used as tracers for studying their transport and metabolism in distinct subcellular compartments with SIMS. Further studies of phenylalanine uptake in human glioblastoma cells may have special significance in boron neutron capture therapy (BNCT) as a boron analogue of phenylalanine, boronophenylalanine is a clinically approved compound for the treatment of brain tumors.

A Framework for Integration of Heterogeneous Medical Imaging Networks

PubMed Central

Viana-Ferreira, Carlos; Ribeiro, Luís S; Costa, Carlos

2014-01-01

Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata. This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS. PMID:25279021

A framework for integration of heterogeneous medical imaging networks.

PubMed

Viana-Ferreira, Carlos; Ribeiro, Luís S; Costa, Carlos

2014-01-01

Medical imaging is increasing its importance in matters of medical diagnosis and in treatment support. Much is due to computers that have revolutionized medical imaging not only in acquisition process but also in the way it is visualized, stored, exchanged and managed. Picture Archiving and Communication Systems (PACS) is an example of how medical imaging takes advantage of computers. To solve problems of interoperability of PACS and medical imaging equipment, the Digital Imaging and Communications in Medicine (DICOM) standard was defined and widely implemented in current solutions. More recently, the need to exchange medical data between distinct institutions resulted in Integrating the Healthcare Enterprise (IHE) initiative that contains a content profile especially conceived for medical imaging exchange: Cross Enterprise Document Sharing for imaging (XDS-i). Moreover, due to application requirements, many solutions developed private networks to support their services. For instance, some applications support enhanced query and retrieve over DICOM objects metadata. This paper proposes anintegration framework to medical imaging networks that provides protocols interoperability and data federation services. It is an extensible plugin system that supports standard approaches (DICOM and XDS-I), but is also capable of supporting private protocols. The framework is being used in the Dicoogle Open Source PACS.

Pretargeted PET Imaging Using a Site-Specifically Labeled Immunoconjugate.

PubMed

Cook, Brendon E; Adumeau, Pierre; Membreno, Rosemery; Carnazza, Kathryn E; Brand, Christian; Reiner, Thomas; Agnew, Brian J; Lewis, Jason S; Zeglis, Brian M

2016-08-17

In recent years, both site-specific bioconjugation techniques and bioorthogonal pretargeting strategies have emerged as exciting technologies with the potential to improve the safety and efficacy of antibody-based nuclear imaging. In the work at hand, we have combined these two approaches to create a pretargeted PET imaging strategy based on the rapid and bioorthogonal inverse electron demand Diels-Alder reaction between a (64)Cu-labeled tetrazine radioligand ((64)Cu-Tz-SarAr) and a site-specifically modified huA33-trans-cyclooctene immunoconjugate ((ss)huA33-PEG12-TCO). A bioconjugation strategy that harnesses enzymatic transformations and strain-promoted azide-alkyne click chemistry was used to site-specifically append PEGylated TCO moieties to the heavy chain glycans of the colorectal cancer-targeting huA33 antibody. Preclinical in vivo validation studies were performed in athymic nude mice bearing A33 antigen-expressing SW1222 human colorectal carcinoma xenografts. To this end, mice were administered (ss)huA33-PEG12-TCO via tail vein injection and-following accumulation intervals of 24 or 48 h-(64)Cu-Tz-SarAr. PET imaging and biodistribution studies reveal that this strategy clearly delineates tumor tissue as early as 1 h post-injection (6.7 ± 1.7%ID/g at 1 h p.i.), producing images with excellent contrast and high tumor-to-background activity concentration ratios (tumor:muscle = 21.5 ± 5.6 at 24 h p.i.). Furthermore, dosimetric calculations illustrate that this pretargeting approach produces only a fraction of the overall effective dose (0.0214 mSv/MBq; 0.079 rem/mCi) of directly labeled radioimmunoconjugates. Ultimately, this method effectively facilitates the high contrast pretargeted PET imaging of colorectal carcinoma using a site-specifically modified immunoconjugate.

Leveraging the crowd for annotation of retinal images.

PubMed

Leifman, George; Swedish, Tristan; Roesch, Karin; Raskar, Ramesh

2015-01-01

Medical data presents a number of challenges. It tends to be unstructured, noisy and protected. To train algorithms to understand medical images, doctors can label the condition associated with a particular image, but obtaining enough labels can be difficult. We propose an annotation approach which starts with a small pool of expertly annotated images and uses their expertise to rate the performance of crowd-sourced annotations. In this paper we demonstrate how to apply our approach for annotation of large-scale datasets of retinal images. We introduce a novel data validation procedure which is designed to cope with noisy ground-truth data and with non-consistent input from both experts and crowd-workers.

Label-free Chemical Imaging of Fungal Spore Walls by Raman Microscopy and Multivariate Curve Resolution Analysis

PubMed Central

Noothalapati, Hemanth; Sasaki, Takahiro; Kaino, Tomohiro; Kawamukai, Makoto; Ando, Masahiro; Hamaguchi, Hiro-o; Yamamoto, Tatsuyuki

2016-01-01

Fungal cell walls are medically important since they represent a drug target site for antifungal medication. So far there is no method to directly visualize structurally similar cell wall components such as α-glucan, β-glucan and mannan with high specificity, especially in a label-free manner. In this study, we have developed a Raman spectroscopy based molecular imaging method and combined multivariate curve resolution analysis to enable detection and visualization of multiple polysaccharide components simultaneously at the single cell level. Our results show that vegetative cell and ascus walls are made up of both α- and β-glucans while spore wall is exclusively made of α-glucan. Co-localization studies reveal the absence of mannans in ascus wall but are distributed primarily in spores. Such detailed picture is believed to further enhance our understanding of the dynamic spore wall architecture, eventually leading to advancements in drug discovery and development in the near future. PMID:27278218

Medical image registration based on normalized multidimensional mutual information

NASA Astrophysics Data System (ADS)

Li, Qi; Ji, Hongbing; Tong, Ming

2009-10-01

Registration of medical images is an essential research topic in medical image processing and applications, and especially a preliminary and key step for multimodality image fusion. This paper offers a solution to medical image registration based on normalized multi-dimensional mutual information. Firstly, affine transformation with translational and rotational parameters is applied to the floating image. Then ordinal features are extracted by ordinal filters with different orientations to represent spatial information in medical images. Integrating ordinal features with pixel intensities, the normalized multi-dimensional mutual information is defined as similarity criterion to register multimodality images. Finally the immune algorithm is used to search registration parameters. The experimental results demonstrate the effectiveness of the proposed registration scheme.

THz Medical Imaging: in vivo Hydration Sensing

PubMed Central

Taylor, Zachary D.; Singh, Rahul S.; Bennett, David B.; Tewari, Priyamvada; Kealey, Colin P.; Bajwa, Neha; Culjat, Martin O.; Stojadinovic, Alexander; Lee, Hua; Hubschman, Jean-Pierre; Brown, Elliott R.; Grundfest, Warren S.

2015-01-01

The application of THz to medical imaging is experiencing a surge in both interest and federal funding. A brief overview of the field is provided along with promising and emerging applications and ongoing research. THz imaging phenomenology is discussed and tradeoffs are identified. A THz medical imaging system, operating at ~525 GHz center frequency with ~125 GHz of response normalized bandwidth is introduced and details regarding principles of operation are provided. Two promising medical applications of THz imaging are presented: skin burns and cornea. For burns, images of second degree, partial thickness burns were obtained in rat models in vivo over an 8 hour period. These images clearly show the formation and progression of edema in and around the burn wound area. For cornea, experimental data measuring the hydration of ex vivo porcine cornea under drying is presented demonstrating utility in ophthalmologic applications. PMID:26085958

Medical microscopic image matching based on relativity

NASA Astrophysics Data System (ADS)

Xie, Fengying; Zhu, Liangen; Jiang, Zhiguo

2003-12-01

In this paper, an effective medical micro-optical image matching algorithm based on relativity is described. The algorithm includes the following steps: Firstly, selecting a sub-area that has obvious character in one of the two images as standard image; Secondly, finding the right matching position in the other image; Thirdly, applying coordinate transformation to merge the two images together. As a kind of application of image matching in medical micro-optical image, this method overcomes the shortcoming of microscope whose visual field is little and makes it possible to watch a big object or many objects in one view. Simultaneously it implements adaptive selection of standard image, and has a satisfied matching speed and result.

Medical Images Remote Consultation

NASA Astrophysics Data System (ADS)

Ferraris, Maurizio; Frixione, Paolo; Squarcia, Sandro

Teleconsultation of digital images among different medical centers is now a reality. The problem to be solved is how to interconnect all the clinical diagnostic devices in a hospital in order to allow physicians and health physicists, working in different places, to discuss on interesting clinical cases visualizing the same diagnostic images at the same time. Applying World Wide Web technologies, the proposed system can be easily used by people with no specific computer knowledge providing a verbose help to guide the user through the right steps of execution. Diagnostic images are retrieved from a relational database or from a standard DICOM-PACS through the DICOM-WWW gateway allowing connection of the usual Web browsers to DICOM applications via the HTTP protocol. The system, which is proposed for radiotherapy implementation, where radiographies play a fundamental role, can be easily converted to different field of medical applications where a remote access to secure data are compulsory.

Radionuclide 131I-labeled multifunctional dendrimers for targeted SPECT imaging and radiotherapy of tumors

NASA Astrophysics Data System (ADS)

Zhu, Jingyi; Zhao, Lingzhou; Cheng, Yongjun; Xiong, Zhijuan; Tang, Yueqin; Shen, Mingwu; Zhao, Jinhua; Shi, Xiangyang

2015-10-01

We report the synthesis, characterization, and utilization of radioactive 131I-labeled multifunctional dendrimers for targeted single-photon emission computed tomography (SPECT) imaging and radiotherapy of tumors. In this study, amine-terminated poly(amidoamine) dendrimers of generation 5 (G5.NH2) were sequentially modified with 3-(4'-hydroxyphenyl)propionic acid-OSu (HPAO) and folic acid (FA) linked with polyethylene glycol (PEG), followed by acetylation modification of the dendrimer remaining surface amines and labeling of radioactive iodine-131 (131I). The generated multifunctional 131I-G5.NHAc-HPAO-PEG-FA dendrimers were characterized via different methods. We show that prior to 131I labeling, the G5.NHAc-HPAO-PEG-FA dendrimers conjugated with approximately 9.4 HPAO moieties per dendrimer are noncytotoxic at a concentration up to 20 μM and are able to target cancer cells overexpressing FA receptors (FAR), thanks to the modified FA ligands. In the presence of a phenol group, radioactive 131I is able to be efficiently labeled onto the dendrimer platform with good stability and high radiochemical purity, and render the platform with an ability for targeted SPECT imaging and radiotherapy of an FAR-overexpressing xenografted tumor model in vivo. The designed strategy to use the facile dendrimer nanotechnology may be extended to develop various radioactive theranostic nanoplatforms for targeted SPECT imaging and radiotherapy of different types of cancer.We report the synthesis, characterization, and utilization of radioactive 131I-labeled multifunctional dendrimers for targeted single-photon emission computed tomography (SPECT) imaging and radiotherapy of tumors. In this study, amine-terminated poly(amidoamine) dendrimers of generation 5 (G5.NH2) were sequentially modified with 3-(4'-hydroxyphenyl)propionic acid-OSu (HPAO) and folic acid (FA) linked with polyethylene glycol (PEG), followed by acetylation modification of the dendrimer remaining surface amines and

Multiscale Medical Image Fusion in Wavelet Domain

PubMed Central

Khare, Ashish

2013-01-01

Wavelet transforms have emerged as a powerful tool in image fusion. However, the study and analysis of medical image fusion is still a challenging area of research. Therefore, in this paper, we propose a multiscale fusion of multimodal medical images in wavelet domain. Fusion of medical images has been performed at multiple scales varying from minimum to maximum level using maximum selection rule which provides more flexibility and choice to select the relevant fused images. The experimental analysis of the proposed method has been performed with several sets of medical images. Fusion results have been evaluated subjectively and objectively with existing state-of-the-art fusion methods which include several pyramid- and wavelet-transform-based fusion methods and principal component analysis (PCA) fusion method. The comparative analysis of the fusion results has been performed with edge strength (Q), mutual information (MI), entropy (E), standard deviation (SD), blind structural similarity index metric (BSSIM), spatial frequency (SF), and average gradient (AG) metrics. The combined subjective and objective evaluations of the proposed fusion method at multiple scales showed the effectiveness and goodness of the proposed approach. PMID:24453868

In vitro labelling and detection of mesenchymal stromal cells: a comparison between magnetic resonance imaging of iron-labelled cells and magnetic resonance spectroscopy of fluorine-labelled cells.

PubMed

Rizzo, Stefania; Petrella, Francesco; Zucca, Ileana; Rinaldi, Elena; Barbaglia, Andrea; Padelli, Francesco; Baggi, Fulvio; Spaggiari, Lorenzo; Bellomi, Massimo; Bruzzone, Maria Grazia

2017-01-01

Among the various stem cell populations used for cell therapy, adult mesenchymal stromal cells (MSCs) have emerged as a major new cell technology. These cells must be tracked after transplantation to monitor their migration within the body and quantify their accumulation at the target site. This study assessed whether rat bone marrow MSCs can be labelled with superparamagnetic iron oxide (SPIO) nanoparticles and perfluorocarbon (PFC) nanoemulsion formulations without altering cell viability and compared magnetic resonance imaging (MRI) and magnetic resonance spectroscopy (MRS) results from iron-labelled and fluorine-labelled MSCs, respectively. Of MSCs, 2 × 10 6 were labelled with Molday ION Rhodamine-B (MIRB) and 2 × 10 6 were labelled with Cell Sense. Cell viability was evaluated by trypan blue exclusion method. Labelled MSCs were divided into four samples containing increasing cell numbers (0.125 × 10 6 , 0.25 × 10 6 , 0.5 × 10 6 , 1 × 10 6 ) and scanned on a 7T MRI: for MIRB-labelled cells, phantoms and cells negative control, T1, T2 and T2* maps were acquired; for Cell Sense labelled cells, phantoms and unlabelled cells, a 19 F non-localised single-pulse MRS sequence was acquired. In total, 86.8% and 83.6% of MIRB-labelled cells and Cell Sense-labelled cells were viable, respectively. MIRB-labelled cells were visible in all samples with different cell numbers; pellets containing 0.5 × 10 6 and 1 × 10 6 of Cell Sense-labelled cells showed a detectable 19 F signal. Our data support the use of both types of contrast material (SPIO and PFC) for MSCs labelling, although further efforts should be dedicated to improve the efficiency of PFC labelling.

An interactive medical image segmentation framework using iterative refinement.

PubMed

Kalshetti, Pratik; Bundele, Manas; Rahangdale, Parag; Jangra, Dinesh; Chattopadhyay, Chiranjoy; Harit, Gaurav; Elhence, Abhay

2017-04-01

Segmentation is often performed on medical images for identifying diseases in clinical evaluation. Hence it has become one of the major research areas. Conventional image segmentation techniques are unable to provide satisfactory segmentation results for medical images as they contain irregularities. They need to be pre-processed before segmentation. In order to obtain the most suitable method for medical image segmentation, we propose MIST (Medical Image Segmentation Tool), a two stage algorithm. The first stage automatically generates a binary marker image of the region of interest using mathematical morphology. This marker serves as the mask image for the second stage which uses GrabCut to yield an efficient segmented result. The obtained result can be further refined by user interaction, which can be done using the proposed Graphical User Interface (GUI). Experimental results show that the proposed method is accurate and provides satisfactory segmentation results with minimum user interaction on medical as well as natural images. Copyright © 2017 Elsevier Ltd. All rights reserved.

Imaging-related medications: a class overview

PubMed Central

2007-01-01

Imaging-related medications (contrast agents) are commonly utilized to improve visualization of radiographic, computed tomography (CT), and magnetic resonance (MR) images. While traditional medications are used specifically for their pharmacological actions, the ideal imaging agent provides enhanced contrast with little biological interaction. The radiopaque agents, barium sulfate and iodinated contrast agents, confer “contrast” to x-ray films by their physical ability to directly absorb x-rays. Gadolinium-based MR agents enhance visualization of tissues when exposed to a magnetic field. Ferrous-ferric oxide–based paramagnetic agents provide negative contrast for MR liver studies. This article provides an overview of clinically relevant information for the imaging-related medications commonly in use. It reviews the safety improvements in new generations of drugs; risk factors and precautions for the reduction of severe adverse reactions (i.e., extravasation, contrast-induced nephropathy, metformin-induced lactic acidosis, and nephrogenic fibrosing dermopathy/nephrogenic systemic fibrosis); and the significance of diligent patient screening before contrast exposure and appropriate monitoring after exposure. PMID:17948119

Multimodality imaging using SPECT/CT and MRI and ligand functionalized 99mTc-labeled magnetic microbubbles

PubMed Central

2013-01-01

Background In the present study, we used multimodal imaging to investigate biodistribution in rats after intravenous administration of a new 99mTc-labeled delivery system consisting of polymer-shelled microbubbles (MBs) functionalized with diethylenetriaminepentaacetic acid (DTPA), thiolated poly(methacrylic acid) (PMAA), chitosan, 1,4,7-triacyclononane-1,4,7-triacetic acid (NOTA), NOTA-super paramagnetic iron oxide nanoparticles (SPION), or DTPA-SPION. Methods Examinations utilizing planar dynamic scintigraphy and hybrid imaging were performed using a commercially available single-photon emission computed tomography (SPECT)/computed tomography (CT) system. For SPION containing MBs, the biodistribution pattern of 99mTc-labeled NOTA-SPION and DTPA-SPION MBs was investigated and co-registered using fusion SPECT/CT and magnetic resonance imaging (MRI). Moreover, to evaluate the biodistribution, organs were removed and radioactivity was measured and calculated as percentage of injected dose. Results SPECT/CT and MRI showed that the distribution of 99mTc-labeled ligand-functionalized MBs varied with the type of ligand as well as with the presence of SPION. The highest uptake was observed in the lungs 1 h post injection of 99mTc-labeled DTPA and chitosan MBs, while a similar distribution to the lungs and the liver was seen after the administration of PMAA MBs. The highest counts of 99mTc-labeled NOTA-SPION and DTPA-SPION MBs were observed in the lungs, liver, and kidneys 1 h post injection. The highest counts were observed in the liver, spleen, and kidneys as confirmed by MRI 24 h post injection. Furthermore, the results obtained from organ measurements were in good agreement with those obtained from SPECT/CT. Conclusions In conclusion, microbubbles functionalized by different ligands can be labeled with radiotracers and utilized for SPECT/CT imaging, while the incorporation of SPION in MB shells enables imaging using MR. Our investigation revealed that biodistribution

Wavelets in medical imaging

NASA Astrophysics Data System (ADS)

Zahra, Noor e.; Sevindir, Hulya Kodal; Aslan, Zafer; Siddiqi, A. H.

2012-07-01

The aim of this study is to provide emerging applications of wavelet methods to medical signals and images, such as electrocardiogram, electroencephalogram, functional magnetic resonance imaging, computer tomography, X-ray and mammography. Interpretation of these signals and images are quite important. Nowadays wavelet methods have a significant impact on the science of medical imaging and the diagnosis of disease and screening protocols. Based on our initial investigations, future directions include neurosurgical planning and improved assessment of risk for individual patients, improved assessment and strategies for the treatment of chronic pain, improved seizure localization, and improved understanding of the physiology of neurological disorders. We look ahead to these and other emerging applications as the benefits of this technology become incorporated into current and future patient care. In this chapter by applying Fourier transform and wavelet transform, analysis and denoising of one of the important biomedical signals like EEG is carried out. The presence of rhythm, template matching, and correlation is discussed by various method. Energy of EEG signal is used to detect seizure in an epileptic patient. We have also performed denoising of EEG signals by SWT.

Imaging Complex Protein Metabolism in Live Organisms by Stimulated Raman Scattering Microscopy with Isotope Labeling

PubMed Central

2016-01-01

Protein metabolism, consisting of both synthesis and degradation, is highly complex, playing an indispensable regulatory role throughout physiological and pathological processes. Over recent decades, extensive efforts, using approaches such as autoradiography, mass spectrometry, and fluorescence microscopy, have been devoted to the study of protein metabolism. However, noninvasive and global visualization of protein metabolism has proven to be highly challenging, especially in live systems. Recently, stimulated Raman scattering (SRS) microscopy coupled with metabolic labeling of deuterated amino acids (D-AAs) was demonstrated for use in imaging newly synthesized proteins in cultured cell lines. Herein, we significantly generalize this notion to develop a comprehensive labeling and imaging platform for live visualization of complex protein metabolism, including synthesis, degradation, and pulse–chase analysis of two temporally defined populations. First, the deuterium labeling efficiency was optimized, allowing time-lapse imaging of protein synthesis dynamics within individual live cells with high spatial–temporal resolution. Second, by tracking the methyl group (CH3) distribution attributed to pre-existing proteins, this platform also enables us to map protein degradation inside live cells. Third, using two subsets of structurally and spectroscopically distinct D-AAs, we achieved two-color pulse–chase imaging, as demonstrated by observing aggregate formation of mutant hungtingtin proteins. Finally, going beyond simple cell lines, we demonstrated the imaging ability of protein synthesis in brain tissues, zebrafish, and mice in vivo. Hence, the presented labeling and imaging platform would be a valuable tool to study complex protein metabolism with high sensitivity, resolution, and biocompatibility for a broad spectrum of systems ranging from cells to model animals and possibly to humans. PMID:25560305

An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases

PubMed Central

Forbes, Jessica L.; Kim, Regina E. Y.; Paulsen, Jane S.; Johnson, Hans J.

2016-01-01

The creation of high-quality medical imaging reference atlas datasets with consistent dense anatomical region labels is a challenging task. Reference atlases have many uses in medical image applications and are essential components of atlas-based segmentation tools commonly used for producing personalized anatomical measurements for individual subjects. The process of manual identification of anatomical regions by experts is regarded as a so-called gold standard; however, it is usually impractical because of the labor-intensive costs. Further, as the number of regions of interest increases, these manually created atlases often contain many small inconsistently labeled or disconnected regions that need to be identified and corrected. This project proposes an efficient process to drastically reduce the time necessary for manual revision in order to improve atlas label quality. We introduce the LabelAtlasEditor tool, a SimpleITK-based open-source label atlas correction tool distributed within the image visualization software 3D Slicer. LabelAtlasEditor incorporates several 3D Slicer widgets into one consistent interface and provides label-specific correction tools, allowing for rapid identification, navigation, and modification of the small, disconnected erroneous labels within an atlas. The technical details for the implementation and performance of LabelAtlasEditor are demonstrated using an application of improving a set of 20 Huntingtons Disease-specific multi-modal brain atlases. Additionally, we present the advantages and limitations of automatic atlas correction. After the correction of atlas inconsistencies and small, disconnected regions, the number of unidentified voxels for each dataset was reduced on average by 68.48%. PMID:27536233

An Open-Source Label Atlas Correction Tool and Preliminary Results on Huntingtons Disease Whole-Brain MRI Atlases.

PubMed

Forbes, Jessica L; Kim, Regina E Y; Paulsen, Jane S; Johnson, Hans J

2016-01-01

The creation of high-quality medical imaging reference atlas datasets with consistent dense anatomical region labels is a challenging task. Reference atlases have many uses in medical image applications and are essential components of atlas-based segmentation tools commonly used for producing personalized anatomical measurements for individual subjects. The process of manual identification of anatomical regions by experts is regarded as a so-called gold standard; however, it is usually impractical because of the labor-intensive costs. Further, as the number of regions of interest increases, these manually created atlases often contain many small inconsistently labeled or disconnected regions that need to be identified and corrected. This project proposes an efficient process to drastically reduce the time necessary for manual revision in order to improve atlas label quality. We introduce the LabelAtlasEditor tool, a SimpleITK-based open-source label atlas correction tool distributed within the image visualization software 3D Slicer. LabelAtlasEditor incorporates several 3D Slicer widgets into one consistent interface and provides label-specific correction tools, allowing for rapid identification, navigation, and modification of the small, disconnected erroneous labels within an atlas. The technical details for the implementation and performance of LabelAtlasEditor are demonstrated using an application of improving a set of 20 Huntingtons Disease-specific multi-modal brain atlases. Additionally, we present the advantages and limitations of automatic atlas correction. After the correction of atlas inconsistencies and small, disconnected regions, the number of unidentified voxels for each dataset was reduced on average by 68.48%.

Automated detection and labeling of high-density EEG electrodes from structural MR images.

PubMed

Marino, Marco; Liu, Quanying; Brem, Silvia; Wenderoth, Nicole; Mantini, Dante

2016-10-01

Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work will contribute to a more widespread use of high

Automated detection and labeling of high-density EEG electrodes from structural MR images

NASA Astrophysics Data System (ADS)

Marino, Marco; Liu, Quanying; Brem, Silvia; Wenderoth, Nicole; Mantini, Dante

2016-10-01

Objective. Accurate knowledge about the positions of electrodes in electroencephalography (EEG) is very important for precise source localizations. Direct detection of electrodes from magnetic resonance (MR) images is particularly interesting, as it is possible to avoid errors of co-registration between electrode and head coordinate systems. In this study, we propose an automated MR-based method for electrode detection and labeling, particularly tailored to high-density montages. Approach. Anatomical MR images were processed to create an electrode-enhanced image in individual space. Image processing included intensity non-uniformity correction, background noise and goggles artifact removal. Next, we defined a search volume around the head where electrode positions were detected. Electrodes were identified as local maxima in the search volume and registered to the Montreal Neurological Institute standard space using an affine transformation. This allowed the matching of the detected points with the specific EEG montage template, as well as their labeling. Matching and labeling were performed by the coherent point drift method. Our method was assessed on 8 MR images collected in subjects wearing a 256-channel EEG net, using the displacement with respect to manually selected electrodes as performance metric. Main results. Average displacement achieved by our method was significantly lower compared to alternative techniques, such as the photogrammetry technique. The maximum displacement was for more than 99% of the electrodes lower than 1 cm, which is typically considered an acceptable upper limit for errors in electrode positioning. Our method showed robustness and reliability, even in suboptimal conditions, such as in the case of net rotation, imprecisely gathered wires, electrode detachment from the head, and MR image ghosting. Significance. We showed that our method provides objective, repeatable and precise estimates of EEG electrode coordinates. We hope our work

Radiation Risk From Medical Imaging

PubMed Central

Lin, Eugene C.

2010-01-01

This review provides a practical overview of the excess cancer risks related to radiation from medical imaging. Primary care physicians should have a basic understanding of these risks. Because of recent attention to this issue, patients are more likely to express concerns over radiation risk. In addition, physicians can play a role in reducing radiation risk to their patients by considering these risks when making imaging referrals. This review provides a brief overview of the evidence pertaining to low-level radiation and excess cancer risks and addresses the radiation doses and risks from common medical imaging studies. Specific subsets of patients may be at greater risk from radiation exposure, and radiation risk should be considered carefully in these patients. Recent technical innovations have contributed to lowering the radiation dose from computed tomography, and the referring physician should be aware of these innovations in making imaging referrals. PMID:21123642

Single cell systems biology by super-resolution imaging and combinatorial labeling

PubMed Central

Lubeck, Eric; Cai, Long

2012-01-01

Fluorescence microscopy is a powerful quantitative tool for exploring regulatory networks in single cells. However, the number of molecular species that can be measured simultaneously is limited by the spectral separability of fluorophores. Here we demonstrate a simple but general strategy to drastically increase the capacity for multiplex detection of molecules in single cells by using optical super-resolution microscopy (SRM) and combinatorial labeling. As a proof of principle, we labeled mRNAs with unique combinations of fluorophores using Fluorescence in situ Hybridization (FISH), and resolved the sequences and combinations of fluorophores with SRM. We measured the mRNA levels of 32 genes simultaneously in single S. cerevisiae cells. These experiments demonstrate that combinatorial labeling and super-resolution imaging of single cells provides a natural approach to bring systems biology into single cells. PMID:22660740

Medical image classification based on multi-scale non-negative sparse coding.

PubMed

Zhang, Ruijie; Shen, Jian; Wei, Fushan; Li, Xiong; Sangaiah, Arun Kumar

2017-11-01

With the rapid development of modern medical imaging technology, medical image classification has become more and more important in medical diagnosis and clinical practice. Conventional medical image classification algorithms usually neglect the semantic gap problem between low-level features and high-level image semantic, which will largely degrade the classification performance. To solve this problem, we propose a multi-scale non-negative sparse coding based medical image classification algorithm. Firstly, Medical images are decomposed into multiple scale layers, thus diverse visual details can be extracted from different scale layers. Secondly, for each scale layer, the non-negative sparse coding model with fisher discriminative analysis is constructed to obtain the discriminative sparse representation of medical images. Then, the obtained multi-scale non-negative sparse coding features are combined to form a multi-scale feature histogram as the final representation for a medical image. Finally, SVM classifier is combined to conduct medical image classification. The experimental results demonstrate that our proposed algorithm can effectively utilize multi-scale and contextual spatial information of medical images, reduce the semantic gap in a large degree and improve medical image classification performance. Copyright © 2017 Elsevier B.V. All rights reserved.

21 CFR 892.2020 - Medical image communications device.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 21 Food and Drugs 8 2010-04-01 2010-04-01 false Medical image communications device. 892.2020 Section 892.2020 Food and Drugs FOOD AND DRUG ADMINISTRATION, DEPARTMENT OF HEALTH AND HUMAN SERVICES (CONTINUED) MEDICAL DEVICES RADIOLOGY DEVICES Diagnostic Devices § 892.2020 Medical image communications...

Content-independent embedding scheme for multi-modal medical image watermarking.

PubMed

Nyeem, Hussain; Boles, Wageeh; Boyd, Colin

2015-02-04

As the increasing adoption of information technology continues to offer better distant medical services, the distribution of, and remote access to digital medical images over public networks continues to grow significantly. Such use of medical images raises serious concerns for their continuous security protection, which digital watermarking has shown great potential to address. We present a content-independent embedding scheme for medical image watermarking. We observe that the perceptual content of medical images varies widely with their modalities. Recent medical image watermarking schemes are image-content dependent and thus they may suffer from inconsistent embedding capacity and visual artefacts. To attain the image content-independent embedding property, we generalise RONI (region of non-interest, to the medical professionals) selection process and use it for embedding by utilising RONI's least significant bit-planes. The proposed scheme thus avoids the need for RONI segmentation that incurs capacity and computational overheads. Our experimental results demonstrate that the proposed embedding scheme performs consistently over a dataset of 370 medical images including their 7 different modalities. Experimental results also verify how the state-of-the-art reversible schemes can have an inconsistent performance for different modalities of medical images. Our scheme has MSSIM (Mean Structural SIMilarity) larger than 0.999 with a deterministically adaptable embedding capacity. Our proposed image-content independent embedding scheme is modality-wise consistent, and maintains a good image quality of RONI while keeping all other pixels in the image untouched. Thus, with an appropriate watermarking framework (i.e., with the considerations of watermark generation, embedding and detection functions), our proposed scheme can be viable for the multi-modality medical image applications and distant medical services such as teleradiology and eHealth.

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope

PubMed Central

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-01-01

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications. PMID:26525841

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope.

PubMed

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-11-03

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications.

Dynamic nano-imaging of label-free living cells using electron beam excitation-assisted optical microscope

NASA Astrophysics Data System (ADS)

Fukuta, Masahiro; Kanamori, Satoshi; Furukawa, Taichi; Nawa, Yasunori; Inami, Wataru; Lin, Sheng; Kawata, Yoshimasa; Terakawa, Susumu

2015-11-01

Optical microscopes are effective tools for cellular function analysis because biological cells can be observed non-destructively and non-invasively in the living state in either water or atmosphere condition. Label-free optical imaging technique such as phase-contrast microscopy has been analysed many cellular functions, and it is essential technology for bioscience field. However, the diffraction limit of light makes it is difficult to image nano-structures in a label-free living cell, for example the endoplasmic reticulum, the Golgi body and the localization of proteins. Here we demonstrate the dynamic imaging of a label-free cell with high spatial resolution by using an electron beam excitation-assisted optical (EXA) microscope. We observed the dynamic movement of the nucleus and nano-scale granules in living cells with better than 100 nm spatial resolution and a signal-to-noise ratio (SNR) around 10. Our results contribute to the development of cellular function analysis and open up new bioscience applications.

A novel method to label preformed liposomes with 64Cu for positron emission tomography (PET) imaging.

PubMed

Seo, Jai Woong; Zhang, Hua; Kukis, David L; Meares, Claude F; Ferrara, Katherine W

2008-12-01

Radiolabeling of liposomes with 64Cu (t(1/2)=12.7 h) is attractive for molecular imaging and monitoring drug delivery. A simple chelation procedure, performed at a low temperature and under mild conditions, is required to radiolabel preloaded liposomes without lipid hydrolysis or the release of the encapsulated contents. Here, we report a 64Cu postlabeling method for liposomes. A 64Cu-specific chelator, 6-[p-(bromoacetamido)benzyl]-1,4,8,11-tetraazacyclotetradecane-N,N',N'',N'''-tetraacetic acid (BAT), was conjugated with an artificial lipid to form a BAT-PEG-lipid. After incorporation of 0.5% (mol/mol) BAT-PEG-lipid during liposome formulation, liposomes were successfully labeled with 64Cu in 0.1 M NH4OAc pH 5 buffer at 35 degrees C for 30-40 min with an incorporation yield as high as 95%. After 48 h of incubation of 64Cu-liposomes in 50/50 serum/PBS solution, more than 88% of the 64Cu label was still associated with liposomes. After injection of liposomal 64Cu in a mouse model, 44+/-6.9, 21+/-2.7, 15+/-2.5, and 7.4+/-1.1 (n=4) % of the injected dose per cubic centimeter remained within the blood pool at 30 min, 18, 28, and 48 h, respectively. The biodistribution at 48 h after injection verified that 7.0+/-0.47 (n=4) and 1.4+/-0.58 (n=3) % of the injected dose per gram of liposomal 64Cu and free 64Cu remained in the blood pool, respectively. Our results suggest that this fast and easy 64Cu labeling of liposomes could be exploited in tracking liposomes in vivo for medical imaging and targeted delivery.

Medical image registration: basic science and clinical implications.

PubMed

Imran, Muhammad Babar; Meo, Sultan Ayoub; Yousuf, Mohammad; Othman, Saleh; Shahid, Abubakar

2010-01-01

Image Registration is a process of aligning two or more images so that corresponding feature can be related objectively. Integration of corresponding and complementary information from various images has become an important area of computation in medical imaging. Merging different images of the same patient taken by different modalities or acquired at different times is quite useful in interpreting lower resolution functional images, such as those provided by nuclear medicine, in determining spatial relationships of structures seen in different modalities. This will help in planning surgery and longitudinal follow up. The aim of this article was to introduce image registration to all those who are working in field of medical sciences in general and medical doctors in particular; and indicate how and where this specialty is moving to provide better health care services.

Supervised restoration of degraded medical images using multiple-point geostatistics.

PubMed

Pham, Tuan D

2012-06-01

Reducing noise in medical images has been an important issue of research and development for medical diagnosis, patient treatment, and validation of biomedical hypotheses. Noise inherently exists in medical and biological images due to the acquisition and transmission in any imaging devices. Being different from image enhancement, the purpose of image restoration is the process of removing noise from a degraded image in order to recover as much as possible its original version. This paper presents a statistically supervised approach for medical image restoration using the concept of multiple-point geostatistics. Experimental results have shown the effectiveness of the proposed technique which has potential as a new methodology for medical and biological image processing. Copyright © 2010 Elsevier Ireland Ltd. All rights reserved.

Preparation of ⁶⁸Ga-labelled DOTA-peptides using a manual labelling approach for small-animal PET imaging.

PubMed

Romero, Eduardo; Martínez, Alfonso; Oteo, Marta; García, Angel; Morcillo, Miguel Angel

2016-01-01

(68)Ga-DOTA-peptides are a promising PET radiotracers used in the detection of different tumours types due to their ability for binding specifically receptors overexpressed in these. Furthermore, (68)Ga can be produced by a (68)Ge/(68)Ga generator on site which is a very good alternative to cyclotron-based PET isotopes. Here, we describe a manual labelling approach for the synthesis of (68)Ga-labelled DOTA-peptides based on concentration and purification of the commercial (68)Ga/(68)Ga generator eluate using an anion exchange-cartridge. (68)Ga-DOTA-TATE was used to image a pheochromocytoma xenograft mouse model by a microPET/CT scanner. The method described provides satisfactory results, allowing the subsequent (68)Ga use to label DOTA-peptides. The simplicity of the method along with its implementation reduced cost, makes it useful in preclinical PET studies. Copyright © 2015 Elsevier Ltd. All rights reserved.

Diagnostic Accuracy of Lumbosacral Spine Magnetic Resonance Image Reading by Chiropractors, Chiropractic Radiologists, and Medical Radiologists.

PubMed

de Zoete, Annemarie; Ostelo, Raymond; Knol, Dirk L; Algra, Paul R; Wilmink, Jan T; van Tulder, Maurits W

2015-06-01

A cross-sectional diagnostic accuracy study was conducted in 2 sessions. It is important to know whether it is possible to accurately detect "specific findings" on lumbosacral magnetic resonance (MR) images and whether the results of different observers are comparable. Health care providers frequently use magnetic resonance imaging in the diagnostic process of patients with low back pain. The use of MR scans is increasing. This leads to an increase in costs and to an increase in risk of inaccurately labeling patients with an anatomical diagnosis that might not be the actual cause of symptoms. A set of 300 blinded MR images was read by medical radiologists, chiropractors, and chiropractic radiologists in 2 sessions. Each assessor read 100 scans in round 1 and 50 scans in round 2. The reference test was an expert panel.For all analyses, the magnetic resonance imaging findings were dichotomized into "specific findings" or "no specific findings." For the agreement, percentage agreement and κ values were calculated and for validity, sensitivity, and specificity. Sensitivity analysis was done for classifications A and B (prevalence of 31% and 57%, respectively). The intraobserver κ values for chiropractors, chiropractic radiologists, and medical radiologists were 0.46, 0.49, and 0.69 for A and 0.55, 0.75, and 0.64 for B, respectively.The interobserver κ values were lowest for chiropractors (0.28 for A, 0.37 for B) and highest for chiropractic radiologists (0.50 for A, 0.49 for B).The sensitivities of the medical radiologists, chiropractors, and chiropractic radiologists were 0.62, 0.71, and 0.75 for A and 0.70, 0.74, 0.84 for B, respectively.The specificities of medical radiologists, chiropractic radiologists, and chiropractors were 0.82, 0.77, and 0.70 for A and 0.74, 0.52, and 0.61 for B, respectively. Agreement and validity of MR image readings of chiropractors and chiropractic and medical radiologists is modest at best. This study supports recommendations in

From Roentgen to magnetic resonance imaging: the history of medical imaging.

PubMed

Scatliff, James H; Morris, Peter J

2014-01-01

Medical imaging has advanced in remarkable ways since the discovery of x-rays 120 years ago. Today's radiologists can image the human body in intricate detail using computed tomography, magnetic resonance imaging, positron emission tomography, ultrasound, and various other modalities. Such technology allows for improved screening, diagnosis, and monitoring of disease, but it also comes with risks. Many imaging modalities expose patients to ionizing radiation, which potentially increases their risk of developing cancer in the future, and imaging may also be associated with possible allergic reactions or risks related to the use of intravenous contrast agents. In addition, the financial costs of imaging are taxing our health care system, and incidental findings can trigger anxiety and further testing. This issue of the NCMJ addresses the pros and cons of medical imaging and discusses in detail the following uses of medical imaging: screening for breast cancer with mammography, screening for osteoporosis and monitoring of bone mineral density with dual-energy x-ray absorptiometry, screening for congenital hip dysplasia in infants with ultrasound, and evaluation of various heart conditions with cardiac imaging. Together, these articles show the challenges that must be met as we seek to harness the power of today's imaging technologies, as well as the potential benefits that can be achieved when these hurdles are overcome.

Label-free in vivo imaging of Drosophila melanogaster by multiphoton microscopy

NASA Astrophysics Data System (ADS)

Lin, Chiao-Ying; Hovhannisyan, Vladimir; Wu, June-Tai; Lin, Sung-Jan; Lin, Chii-Wann; Chen, Jyh-Horng; Dong, Chen-Yuan

2008-02-01

The fruit fly Drosophila melanogaster is one of the most valuable organisms in genetic and developmental biology studies. Drosophila is a small organism with a short life cycle, and is inexpensive and easy to maintain. The entire genome of Drosophila has recently been sequenced (cite the reference). These advantages make fruit fly an attractive model organism for biomedical researches. Unlike humans, Drosophila can be subjected to genetic manipulation with relative ease. Originally, Drosophila was mostly used in classical genetics studies. In the model era of molecular biology, the fruit fly has become a model organ for developmental biology researches. In the past, numerous molecularly modified mutants with well defined genetic defects affecting different aspects of the developmental processes have been identified and studied. However, traditionally, the developmental defects of the mutant flies are mostly examined in isolated fixed tissues which preclude the observation of the dynamic interaction of the different cell types and the extracellular matrix. Therefore, the ability to image different organelles of the fruit fly without extrinsic labeling is invaluable for Drosophila biology. In this work, we successfully acquire in vivo images of both developing muscles and axons of motor neurons in the three larval stages by using the minimially invasive imaging modality of multiphoton (SHG) microscopy. We found that while SHG imaging is useful in revealing the muscular architecture of the developing larva, it is the autofluorescence signal that allows label-free imaging of various organelles to be achieved. Our results demonstrate that multiphoton imaging is a powerful technique for investigation the development of Drosophila.

Micrometer-sized iron oxide particle labeling of mesenchymal stem cells for magnetic resonance imaging-based monitoring of cartilage tissue engineering.

PubMed

Saldanha, Karl J; Doan, Ryan P; Ainslie, Kristy M; Desai, Tejal A; Majumdar, Sharmila

2011-01-01

To examine mesenchymal stem cell (MSC) labeling with micrometer-sized iron oxide particles (MPIOs) for magnetic resonance imaging (MRI)-based tracking and its application to monitoring articular cartilage regeneration. Rabbit MSCs were labeled using commercial MPIOs. In vitro MRI was performed with gradient echo (GRE) and spin echo (SE) sequences at 3T and quantitatively characterized using line profile and region of interest analysis. Ex vivo MRI of hydrogel-encapsulated labeled MSCs implanted within a bovine knee was performed with spoiled GRE (SPGR) and T(1ρ) sequences. Fluorescence microscopy, labeling efficiency, and chondrogenesis of MPIO-labeled cells were also examined. MPIO labeling results in efficient contrast uptake and signal loss that can be visualized and quantitatively characterized via MRI. SPGR imaging of implanted cells results in ex vivo detection within native tissue, and T(1ρ) imaging is unaffected by the presence of labeled cells immediately following implantation. MPIO labeling does not affect quantitative glycosaminoglycan production during chondrogenesis, but iron aggregation hinders extracellular matrix visualization. This aggregation may result from excess unincorporated particles following labeling and is an issue that necessitates further investigation. This study demonstrates the promise of MPIO labeling for monitoring cartilage regeneration and highlights its potential in the development of cell-based tissue engineering strategies. Published by Elsevier Inc.

Radiology and Enterprise Medical Imaging Extensions (REMIX).

PubMed

Erdal, Barbaros S; Prevedello, Luciano M; Qian, Songyue; Demirer, Mutlu; Little, Kevin; Ryu, John; O'Donnell, Thomas; White, Richard D

2018-02-01

Radiology and Enterprise Medical Imaging Extensions (REMIX) is a platform originally designed to both support the medical imaging-driven clinical and clinical research operational needs of Department of Radiology of The Ohio State University Wexner Medical Center. REMIX accommodates the storage and handling of "big imaging data," as needed for large multi-disciplinary cancer-focused programs. The evolving REMIX platform contains an array of integrated tools/software packages for the following: (1) server and storage management; (2) image reconstruction; (3) digital pathology; (4) de-identification; (5) business intelligence; (6) texture analysis; and (7) artificial intelligence. These capabilities, along with documentation and guidance, explaining how to interact with a commercial system (e.g., PACS, EHR, commercial database) that currently exists in clinical environments, are to be made freely available.

RayPlus: a Web-Based Platform for Medical Image Processing.

PubMed

Yuan, Rong; Luo, Ming; Sun, Zhi; Shi, Shuyue; Xiao, Peng; Xie, Qingguo

2017-04-01

Medical image can provide valuable information for preclinical research, clinical diagnosis, and treatment. As the widespread use of digital medical imaging, many researchers are currently developing medical image processing algorithms and systems in order to accommodate a better result to clinical community, including accurate clinical parameters or processed images from the original images. In this paper, we propose a web-based platform to present and process medical images. By using Internet and novel database technologies, authorized users can easily access to medical images and facilitate their workflows of processing with server-side powerful computing performance without any installation. We implement a series of algorithms of image processing and visualization in the initial version of Rayplus. Integration of our system allows much flexibility and convenience for both research and clinical communities.

Carbon-11 and Fluorine-18 Labeled Amino Acid Tracers for Positron Emission Tomography Imaging of Tumors

NASA Astrophysics Data System (ADS)

Sun, Aixia; Liu, Xiang; Tang, Ganghua

2017-12-01

Tumor cells have an increased nutritional demand for amino acids(AAs) to satisfy their rapid proliferation. Positron-emitting nuclide labeled AAs are interesting probes and are of great importance for imaging tumors using positron emission tomography (PET). Carbon-11 and fluorine-18 labeled AAs include the [1-11C] amino acids, labeling alpha-C- amino acids, the branched-chain of amino acids and N-substituted carbon-11 labeled amino acids. These tracers target protein synthesis or amino acid(AA) transport, and their uptake mechanism mainly involves AA transport. AA PET tracers have been widely used in clinical settings to image brain tumors, neuroendocrine tumors, prostate cancer, breast cancer, non–small cell lung cancer (NSCLC) and hepatocellular carcinoma. This review focuses on the fundamental concepts and the uptake mechanism of AAs, AA PET tracers and their clinical applications.

Radically Reducing Radiation Exposure during Routine Medical Imaging

Cancer.gov

Exposure to radiation from medical imaging in the United States has increased dramatically. NCI and several partner organizations sponsored a 2011 summit to promote efforts to reduce radiation exposure from medical imaging.

Rapid Synthesis of 68Ga-labeled macroaggregated human serum albumin (MAA) for routine application in perfusion imaging using PET/CT.

PubMed

Mueller, D; Kulkarni, Harshad; Baum, Richard P; Odparlik, Andreas

2017-04-01

99m Tc-labeled MAA is commonly used for single photon emission computed tomography SPECT. In contrast, positron emission tomography/CT (PET/CT) delivers images with significantly higher resolution. The generator produced radionuclide 68 Ga is widely used for PET/CT imaging agents and 68 Ga-labeled MAA represents an attractive alternative to 99m Tc-labeled MAA. We report a simple and rapid NaCl based labeling procedure for the labeling of MAA with 68 Ga using a commercially available MAA labeling kit for 99m Tc. The procedure delivers 68 Ga-labeled MAA with a high specific activity and a high labeling efficiency (>99%). The synthesis does not require a final step of separation or the use of organic solvents. Copyright © 2017 Elsevier Ltd. All rights reserved.

Nitrogen-13-labeled ammonia for myocardial imaging.

PubMed

Walsh, W F; Fill, H R; Harper, P V

1977-01-01

Cyclotron-produced nitrogen-13 (half-life 10 min), as labeled ammonia (13NH4+), has been evaluated as a myocardial perfusion imaging agent. The regional myocardial uptake of 13NH4+ has been shown to be proportional to regional tissue perfusion in animal studies. Intravenously administered 13NH4+ is rapidly cleared from the circulation, being extracted by the liver (15%), lungs, myocardium (2%-4%), brain, kidney, and bladder. Myocardial ammonia is metabolized mainly to glutamine via the glutamine synthetase pathway. Pulmonary uptake is substantial, but usually transient, except in smokers where clearance may be delayed. The position annihilation irradiation (511 keV) of 13N may be imaged with a scintillation camera, using either a specially designed tungsten collimator or a pinhole collimator. After early technical problems with collimation and the production method of 13NH4+ were overcome, reproducible high quality myocardial images were consistently obtained. The normal myocardial image was established to be of a homogeneous "doughnut" configuration. Imaging studies performed in patients with varying manifestations of ischemic and valvular heart disease showed a high incidence of localized perfusion defects, especially in patients with acute myocardial infarction. Sequential studies at short intervals in patients with acute infarction showed correlation between alterations in regional perfusion and the clinical course of the patient. It is concluded that myocardial imaging with 13NH4+ and a scintillation camera provides a valid and noninvasive means of assessing regional myocardial perfusion. This method is especially suitable for sequential studies of acute cardiac patients at short intervals. Coincidence imaging of the 511 keV annihilation irradiation provides a tomographic and potentially quantitative assessment of the regional myocardial uptake of 13NH4+.

Consumer perceptions of medication warnings about driving: a comparison of French and Australian labels.

PubMed

Smyth, T; Sheehan, M; Siskind, V; Mercier-Guyon, C; Mallaret, M

2013-01-01

Little research has examined user perceptions of medication warnings about driving. Consumer perceptions of the Australian national approach to medication warnings about driving are examined. The Australian approach to warning presentation is compared with an alternative approach used in France. Visual characteristics of the warnings and overall warning readability are investigated. Risk perceptions and behavioral intentions associated with the warnings are also examined. Surveys were conducted with 358 public hospital outpatients in Queensland, Australia. Extending this investigation is a supplementary comparison study of French hospital outpatients (n = 75). The results suggest that the Australian warning approach of using a combination of visual characteristics is important for consumers but that the use of a pictogram could enhance effects. Significantly higher levels of risk perception were found among the sample for the French highest severity label compared to the analogous mandatory Australian warning, with a similar trend evident in the French study results. The results also indicated that the French label was associated with more cautious behavioral intentions. The results are potentially important for the Australian approach to medication warnings about driving impairment. The research contributes practical findings that can be used to enhance the effectiveness of warnings and develop countermeasures in this area. Hospital pharmacy patients should include persons with the highest level of likelihood of knowledge and awareness of medication warning labeling. Even in this context it appears that a review of the Australian warning system would be useful particularly in the context of increasing evidence relating to associated driving risks. Reviewing text size and readability of messages including the addition of pictograms, as well as clarifying the importance of potential risk in a general community context, is recommended for consideration and further

Update on radionuclide imaging in hepatobiliary disease. [/sup 99m/Tc-labelled acetanilide iminodracetic acid analogues

DOE Office of Scientific and Technical Information (OSTI.GOV)

Rosenthall, L.

1981-05-01

The recent introduction of technetium Tc 99m-labeled acetanilide iminodiacetic acid (/sup 99m/Tc-IDA) analogues has facilitated the clincal study of the bile flow pathways. A variety of /sup 99m/Tc-IDA derivaties are under investigation. Basically all are metabolized by the hepatocyte and immediately thereafter excreted unconjugated into the biliary tract. Of the various derivatives tested, e.g., dimethyl (lidofenin), diethyl, paraisopropyl (iprofenin), parabutyl (butilfenin), and diisopropyl (disofenin), the last named is the best universal agent at this time. By serial liver imaging the patency of the cystic duct and the integrity of altered cholangiointestinal anatomy can be assessed, leakage of bile and gastricmore » reflux can be disclosed, and medical and surgical jaundice can be distinguished.« less

Quantitative label-free multimodality nonlinear optical imaging for in situ differentiation of cancerous lesions

NASA Astrophysics Data System (ADS)

Xu, Xiaoyun; Li, Xiaoyan; Cheng, Jie; Liu, Zhengfan; Thrall, Michael J.; Wang, Xi; Wang, Zhiyong; Wong, Stephen T. C.

2013-03-01

The development of real-time, label-free imaging techniques has recently attracted research interest for in situ differentiation of cancerous lesions from normal tissues. Molecule-specific intrinsic contrast can arise from label-free imaging techniques such as Coherent Anti-Stokes Raman Scattering (CARS), Two-Photon Excited AutoFluorescence (TPEAF), and Second Harmonic Generation (SHG), which, in combination, would hold the promise of a powerful label-free tool for cancer diagnosis. Among cancer-related deaths, lung carcinoma is the leading cause for both sexes. Although early treatment can increase the survival rate dramatically, lesion detection and precise diagnosis at an early stage is unusual due to its asymptomatic nature and limitations of current diagnostic techniques that make screening difficult. We investigated the potential of using multimodality nonlinear optical microscopy that incorporates CARS, TPEAF, and SHG techniques for differentiation of lung cancer from normal tissue. Cancerous and non-cancerous lung tissue samples from patients were imaged using CARS, TPEAF, and SHG techniques for comparison. These images showed good pathology correlation with hematoxylin and eosin (H and E) stained sections from the same tissue samples. Ongoing work includes imaging at various penetration depths to show three-dimensional morphologies of tumor cell nuclei using CARS, elastin using TPEAF, and collagen using SHG and developing classification algorithms for quantitative feature extraction to enable lung cancer diagnosis. Our results indicate that via real-time morphology analyses, a multimodality nonlinear optical imaging platform potentially offers a powerful minimally-invasive way to differentiate cancer lesions from surrounding non-tumor tissues in vivo for clinical applications.

Large-scale retrieval for medical image analytics: A comprehensive review.

PubMed

Li, Zhongyu; Zhang, Xiaofan; Müller, Henning; Zhang, Shaoting

2018-01-01

Over the past decades, medical image analytics was greatly facilitated by the explosion of digital imaging techniques, where huge amounts of medical images were produced with ever-increasing quality and diversity. However, conventional methods for analyzing medical images have achieved limited success, as they are not capable to tackle the huge amount of image data. In this paper, we review state-of-the-art approaches for large-scale medical image analysis, which are mainly based on recent advances in computer vision, machine learning and information retrieval. Specifically, we first present the general pipeline of large-scale retrieval, summarize the challenges/opportunities of medical image analytics on a large-scale. Then, we provide a comprehensive review of algorithms and techniques relevant to major processes in the pipeline, including feature representation, feature indexing, searching, etc. On the basis of existing work, we introduce the evaluation protocols and multiple applications of large-scale medical image retrieval, with a variety of exploratory and diagnostic scenarios. Finally, we discuss future directions of large-scale retrieval, which can further improve the performance of medical image analysis. Copyright © 2017 Elsevier B.V. All rights reserved.

Denoising Medical Images using Calculus of Variations

PubMed Central

Kohan, Mahdi Nakhaie; Behnam, Hamid

2011-01-01

We propose a method for medical image denoising using calculus of variations and local variance estimation by shaped windows. This method reduces any additive noise and preserves small patterns and edges of images. A pyramid structure-texture decomposition of images is used to separate noise and texture components based on local variance measures. The experimental results show that the proposed method has visual improvement as well as a better SNR, RMSE and PSNR than common medical image denoising methods. Experimental results in denoising a sample Magnetic Resonance image show that SNR, PSNR and RMSE have been improved by 19, 9 and 21 percents respectively. PMID:22606674

Tissues segmentation based on multi spectral medical images

NASA Astrophysics Data System (ADS)

Li, Ya; Wang, Ying

2017-11-01

Each band image contains the most obvious tissue feature according to the optical characteristics of different tissues in different specific bands for multispectral medical images. In this paper, the tissues were segmented by their spectral information at each multispectral medical images. Four Local Binary Patter descriptors were constructed to extract blood vessels based on the gray difference between the blood vessels and their neighbors. The segmented tissue in each band image was merged to a clear image.

Label-free imaging and spectroscopy for early detection of cervical cancer.

PubMed

Jing, Yueyue; Wang, Yulan; Wang, Xinyi; Song, Chuan; Ma, Jiong; Xie, Yonghui; Fei, Yiyan; Zhang, Qinghua; Mi, Lan

2018-05-01

The label-free imaging and spectroscopy method was studied on cervical unstained tissue sections obtained from 36 patients. The native fluorescence spectra of tissues are analyzed by the optical redox ratio (ORR), which is defined as fluorescence intensity ratio between NADH and FAD, and indicates the metabolism change with the cancer development. The ORRs of normal tissues are consistently higher than those of precancer or cancerous tissues. A criterion line of ORR at 5.0 can be used to discriminate cervical precancer/cancer from normal tissues. The sensitivity and specificity of the native fluorescence spectroscopy method for cervical cancer diagnosis are determined as 100% and 91%. Moreover, the native fluorescence spectroscopy study is much more sensitive on the healthy region of cervical precancer/cancer patients compared with the traditional clinical staining method. The results suggest label-free imaging and spectroscopy is a fast, highly sensitive and specific method on the detection of cervical cancer. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Dynamic PET and Optical Imaging and Compartment Modeling using a Dual-labeled Cyclic RGD Peptide Probe

PubMed Central

Zhu, Lei; Guo, Ning; Li, Quanzheng; Ma, Ying; Jacboson, Orit; Lee, Seulki; Choi, Hak Soo; Mansfield, James R.; Niu, Gang; Chen, Xiaoyuan

2012-01-01

Purpose: The aim of this study is to determine if dynamic optical imaging could provide comparable kinetic parameters to that of dynamic PET imaging by a near-infrared dye/64Cu dual-labeled cyclic RGD peptide. Methods: The integrin αvβ3 binding RGD peptide was conjugated with a macrocyclic chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) for copper labeling and PET imaging and a near-infrared dye ZW-1 for optical imaging. The in vitro biological activity of RGD-C(DOTA)-ZW-1 was characterized by cell staining and receptor binding assay. Sixty-min dynamic PET and optical imaging were acquired on a MDA-MB-435 tumor model. Singular value decomposition (SVD) method was applied to compute the dynamic optical signal from the two-dimensional optical projection images. Compartment models were used to quantitatively analyze and compare the dynamic optical and PET data. Results: The dual-labeled probe 64Cu-RGD-C(DOTA)-ZW-1 showed integrin specific binding in vitro and in vivo. The binding potential (Bp) derived from dynamic optical imaging (1.762 ± 0.020) is comparable to that from dynamic PET (1.752 ± 0.026). Conclusion: The signal un-mixing process using SVD improved the accuracy of kinetic modeling of 2D dynamic optical data. Our results demonstrate that 2D dynamic optical imaging with SVD analysis could achieve comparable quantitative results as dynamic PET imaging in preclinical xenograft models. PMID:22916074

Dynamic PET and Optical Imaging and Compartment Modeling using a Dual-labeled Cyclic RGD Peptide Probe.

PubMed

Zhu, Lei; Guo, Ning; Li, Quanzheng; Ma, Ying; Jacboson, Orit; Lee, Seulki; Choi, Hak Soo; Mansfield, James R; Niu, Gang; Chen, Xiaoyuan

2012-01-01

The aim of this study is to determine if dynamic optical imaging could provide comparable kinetic parameters to that of dynamic PET imaging by a near-infrared dye/(64)Cu dual-labeled cyclic RGD peptide. The integrin α(v)β(3) binding RGD peptide was conjugated with a macrocyclic chelator 1,4,7,10-tetraazacyclododecane-1,4,7,10-tetraacetic acid (DOTA) for copper labeling and PET imaging and a near-infrared dye ZW-1 for optical imaging. The in vitro biological activity of RGD-C(DOTA)-ZW-1 was characterized by cell staining and receptor binding assay. Sixty-min dynamic PET and optical imaging were acquired on a MDA-MB-435 tumor model. Singular value decomposition (SVD) method was applied to compute the dynamic optical signal from the two-dimensional optical projection images. Compartment models were used to quantitatively analyze and compare the dynamic optical and PET data. The dual-labeled probe (64)Cu-RGD-C(DOTA)-ZW-1 showed integrin specific binding in vitro and in vivo. The binding potential (Bp) derived from dynamic optical imaging (1.762 ± 0.020) is comparable to that from dynamic PET (1.752 ± 0.026). The signal un-mixing process using SVD improved the accuracy of kinetic modeling of 2D dynamic optical data. Our results demonstrate that 2D dynamic optical imaging with SVD analysis could achieve comparable quantitative results as dynamic PET imaging in preclinical xenograft models.

High-level production of C-11-carboxyl-labeled amino acids. [For use in tumor and pancreatic imaging

DOE Office of Scientific and Technical Information (OSTI.GOV)

Washburn, L. C.; Sun, T. T.; Byrd, B. L.

Carbon-11-labeled amino acids have significant potential as agents for positron tomographic functional imaging. We have developed a rapid, high-temperature, high-pressure modification of the Buecherer--Strecker amino acid synthesis and found it to be quite general for the production of C-11-carboxyl-labeled neutral amino acids. Production of C-11-carboxyl-labeled DL-tryptophan requires certain modifications in the procedure. Twelve different amino acids have been produced to date by this technique. Synthesis and chromatographic purification require approximately 40 min, and C-11-carboxyl-labeled amino acids have been produced in yields of up to 425 mCi. Two C-11-carboxyl-labeled amino acids are being investigated clinically for tumor scanning and two othersmore » for pancreatic imaging. Over 120 batches of the various agents have been produced for clinical use over a three-year period.« less

[Research Progress of Multi-Model Medical Image Fusion at Feature Level].

PubMed

Zhang, Junjie; Zhou, Tao; Lu, Huiling; Wang, Huiqun

2016-04-01

Medical image fusion realizes advantage integration of functional images and anatomical images.This article discusses the research progress of multi-model medical image fusion at feature level.We firstly describe the principle of medical image fusion at feature level.Then we analyze and summarize fuzzy sets,rough sets,D-S evidence theory,artificial neural network,principal component analysis and other fusion methods’ applications in medical image fusion and get summery.Lastly,we in this article indicate present problems and the research direction of multi-model medical images in the future.

The use of radiocobalt as a label improves imaging of EGFR using DOTA-conjugated Affibody molecule.

PubMed

Garousi, Javad; Andersson, Ken G; Dam, Johan H; Olsen, Birgitte B; Mitran, Bogdan; Orlova, Anna; Buijs, Jos; Ståhl, Stefan; Löfblom, John; Thisgaard, Helge; Tolmachev, Vladimir

2017-07-20

Several anti-cancer therapies target the epidermal growth factor receptor (EGFR). Radionuclide imaging of EGFR expression in tumours may aid in selection of optimal cancer therapy. The 111 In-labelled DOTA-conjugated Z EGFR:2377 Affibody molecule was successfully used for imaging of EGFR-expressing xenografts in mice. An optimal combination of radionuclide, chelator and targeting protein may further improve the contrast of radionuclide imaging. The aim of this study was to evaluate the targeting properties of radiocobalt-labelled DOTA-Z EGFR:2377 . DOTA-Z EGFR:2377 was labelled with 57 Co (T 1/2  = 271.8 d), 55 Co (T 1/2  = 17.5 h), and, for comparison, with the positron-emitting radionuclide 68 Ga (T 1/2  = 67.6 min) with preserved specificity of binding to EGFR-expressing A431 cells. The long-lived cobalt radioisotope 57 Co was used in animal studies. Both 57 Co-DOTA-Z EGFR:2377 and 68 Ga-DOTA-Z EGFR:2377 demonstrated EGFR-specific accumulation in A431 xenografts and EGFR-expressing tissues in mice. Tumour-to-organ ratios for the radiocobalt-labelled DOTA-Z EGFR:2377 were significantly higher than for the gallium-labelled counterpart already at 3 h after injection. Importantly, 57 Co-DOTA-Z EGFR:2377 demonstrated a tumour-to-liver ratio of 3, which is 7-fold higher than the tumour-to-liver ratio for 68 Ga-DOTA-Z EGFR:2377 . The results of this study suggest that the positron-emitting cobalt isotope 55 Co would be an optimal label for DOTA-Z EGFR:2377 and further development should concentrate on this radionuclide as a label.

Multiview Locally Linear Embedding for Effective Medical Image Retrieval

PubMed Central

Shen, Hualei; Tao, Dacheng; Ma, Dianfu

2013-01-01

Content-based medical image retrieval continues to gain attention for its potential to assist radiological image interpretation and decision making. Many approaches have been proposed to improve the performance of medical image retrieval system, among which visual features such as SIFT, LBP, and intensity histogram play a critical role. Typically, these features are concatenated into a long vector to represent medical images, and thus traditional dimension reduction techniques such as locally linear embedding (LLE), principal component analysis (PCA), or laplacian eigenmaps (LE) can be employed to reduce the “curse of dimensionality”. Though these approaches show promising performance for medical image retrieval, the feature-concatenating method ignores the fact that different features have distinct physical meanings. In this paper, we propose a new method called multiview locally linear embedding (MLLE) for medical image retrieval. Following the patch alignment framework, MLLE preserves the geometric structure of the local patch in each feature space according to the LLE criterion. To explore complementary properties among a range of features, MLLE assigns different weights to local patches from different feature spaces. Finally, MLLE employs global coordinate alignment and alternating optimization techniques to learn a smooth low-dimensional embedding from different features. To justify the effectiveness of MLLE for medical image retrieval, we compare it with conventional spectral embedding methods. We conduct experiments on a subset of the IRMA medical image data set. Evaluation results show that MLLE outperforms state-of-the-art dimension reduction methods. PMID:24349277

Intelligent retrieval of medical images from the Internet

NASA Astrophysics Data System (ADS)

Tang, Yau-Kuo; Chiang, Ted T.

1996-05-01

The object of this study is using Internet resources to provide a cost-effective, user-friendly method to access the medical image archive system and to provide an easy method for the user to identify the images required. This paper describes the prototype system architecture, the implementation, and results. In the study, we prototype the Intelligent Medical Image Retrieval (IMIR) system as a Hypertext Transport Prototype server and provide Hypertext Markup Language forms for user, as an Internet client, using browser to enter image retrieval criteria for review. We are developing the intelligent retrieval engine, with the capability to map the free text search criteria to the standard terminology used for medical image identification. We evaluate retrieved records based on the number of the free text entries matched and their relevance level to the standard terminology. We are in the integration and testing phase. We have collected only a few different types of images for testing and have trained a few phrases to map the free text to the standard medical terminology. Nevertheless, we are able to demonstrate the IMIR's ability to search, retrieve, and review medical images from the archives using general Internet browser. The prototype also uncovered potential problems in performance, security, and accuracy. Additional studies and enhancements will make the system clinically operational.

SPIO-labeled Yttrium Microspheres for MR Imaging Quantification of Transcatheter Intrahepatic Delivery in a Rodent Model

PubMed Central

Li, Weiguo; Zhang, Zhuoli; Gordon, Andrew C.; Chen, Jeane; Nicolai, Jodi; Lewandowski, Robert J.; Omary, Reed A.

2016-01-01

Purpose To investigate the qualitative and quantitative impacts of labeling yttrium microspheres with increasing amounts of superparamagnetic iron oxide (SPIO) material for magnetic resonance (MR) imaging in phantom and rodent models. Materials and Methods Animal model studies were approved by the institutional Animal Care and Use Committee. The r2* relaxivity for each of four microsphere SPIO compositions was determined from 32 phantoms constructed with agarose gel and in eight concentrations from each of the four compositions. Intrahepatic transcatheter infusion procedures were performed in rats by using each of the four compositions before MR imaging to visualize distributions within the liver. For quantitative studies, doses of 5, 10, 15, or 20 mg 2% SPIO-labeled yttrium microspheres were infused into 24 rats (six rats per group). MR imaging R2* measurements were used to quantify the dose delivered to each liver. Pearson correlation, analysis of variance, and intraclass correlation analyses were performed to compare MR imaging measurements in phantoms and animal models. Results Increased r2* relaxivity was observed with incremental increases of SPIO microsphere content. R2* measurements of the 2% SPIO–labeled yttrium microsphere concentration were well correlated with known phantom concentrations (R2 = 1.00, P < .001) over a broader linear range than observed for the other three compositions. Microspheres were heterogeneously distributed within each liver; increasing microsphere SPIO content produced marked signal voids. R2*-based measurements of 2% SPIO–labeled yttrium microsphere delivery were well correlated with infused dose (intraclass correlation coefficient, 0.98; P < .001). Conclusion MR imaging R2* measurements of yttrium microspheres labeled with 2% SPIO can quantitatively depict in vivo intrahepatic biodistribution in a rat model. © RSNA, 2015 Online supplemental material is available for this article. PMID:26313619

Ontology-guided organ detection to retrieve web images of disease manifestation: towards the construction of a consumer-based health image library.

PubMed

Chen, Yang; Ren, Xiaofeng; Zhang, Guo-Qiang; Xu, Rong

2013-01-01

Visual information is a crucial aspect of medical knowledge. Building a comprehensive medical image base, in the spirit of the Unified Medical Language System (UMLS), would greatly benefit patient education and self-care. However, collection and annotation of such a large-scale image base is challenging. To combine visual object detection techniques with medical ontology to automatically mine web photos and retrieve a large number of disease manifestation images with minimal manual labeling effort. As a proof of concept, we first learnt five organ detectors on three detection scales for eyes, ears, lips, hands, and feet. Given a disease, we used information from the UMLS to select affected body parts, ran the pretrained organ detectors on web images, and combined the detection outputs to retrieve disease images. Compared with a supervised image retrieval approach that requires training images for every disease, our ontology-guided approach exploits shared visual information of body parts across diseases. In retrieving 2220 web images of 32 diseases, we reduced manual labeling effort to 15.6% while improving the average precision by 3.9% from 77.7% to 81.6%. For 40.6% of the diseases, we improved the precision by 10%. The results confirm the concept that the web is a feasible source for automatic disease image retrieval for health image database construction. Our approach requires a small amount of manual effort to collect complex disease images, and to annotate them by standard medical ontology terms.

Vessel segmentation in 4D arterial spin labeling magnetic resonance angiography images of the brain

NASA Astrophysics Data System (ADS)

Phellan, Renzo; Lindner, Thomas; Falcão, Alexandre X.; Forkert, Nils D.

2017-03-01

4D arterial spin labeling magnetic resonance angiography (4D ASL MRA) is a non-invasive and safe modality for cerebrovascular imaging procedures. It uses the patient's magnetically labeled blood as intrinsic contrast agent, so that no external contrast media is required. It provides important 3D structure and blood flow information but a sufficient cerebrovascular segmentation is important since it can help clinicians to analyze and diagnose vascular diseases faster, and with higher confidence as compared to simple visual rating of raw ASL MRA images. This work presents a new method for automatic cerebrovascular segmentation in 4D ASL MRA images of the brain. In this process images are denoised, corresponding image label/control image pairs of the 4D ASL MRA sequences are subtracted, and temporal intensity averaging is used to generate a static representation of the vascular system. After that, sets of vessel and background seeds are extracted and provided as input for the image foresting transform algorithm to segment the vascular system. Four 4D ASL MRA datasets of the brain arteries of healthy subjects and corresponding time-of-flight (TOF) MRA images were available for this preliminary study. For evaluation of the segmentation results of the proposed method, the cerebrovascular system was automatically segmented in the high-resolution TOF MRA images using a validated algorithm and the segmentation results were registered to the 4D ASL datasets. Corresponding segmentation pairs were compared using the Dice similarity coefficient (DSC). On average, a DSC of 0.9025 was achieved, indicating that vessels can be extracted successfully from 4D ASL MRA datasets by the proposed segmentation method.

Monte Carlo simulations of medical imaging modalities

DOE Office of Scientific and Technical Information (OSTI.GOV)

Estes, G.P.

Because continuous-energy Monte Carlo radiation transport calculations can be nearly exact simulations of physical reality (within data limitations, geometric approximations, transport algorithms, etc.), it follows that one should be able to closely approximate the results of many experiments from first-principles computations. This line of reasoning has led to various MCNP studies that involve simulations of medical imaging modalities and other visualization methods such as radiography, Anger camera, computerized tomography (CT) scans, and SABRINA particle track visualization. It is the intent of this paper to summarize some of these imaging simulations in the hope of stimulating further work, especially as computermore » power increases. Improved interpretation and prediction of medical images should ultimately lead to enhanced medical treatments. It is also reasonable to assume that such computations could be used to design new or more effective imaging instruments.« less

Segmentation of medical images using explicit anatomical knowledge

NASA Astrophysics Data System (ADS)

Wilson, Laurie S.; Brown, Stephen; Brown, Matthew S.; Young, Jeanne; Li, Rongxin; Luo, Suhuai; Brandt, Lee

1999-07-01

Knowledge-based image segmentation is defined in terms of the separation of image analysis procedures and representation of knowledge. Such architecture is particularly suitable for medical image segmentation, because of the large amount of structured domain knowledge. A general methodology for the application of knowledge-based methods to medical image segmentation is described. This includes frames for knowledge representation, fuzzy logic for anatomical variations, and a strategy for determining the order of segmentation from the modal specification. This method has been applied to three separate problems, 3D thoracic CT, chest X-rays and CT angiography. The application of the same methodology to such a range of applications suggests a major role in medical imaging for segmentation methods incorporating representation of anatomical knowledge.

Cell labeling with magnetic nanoparticles: Opportunity for magnetic cell imaging and cell manipulation

PubMed Central

2013-01-01

This tutorial describes a method of controlled cell labeling with citrate-coated ultra small superparamagnetic iron oxide nanoparticles. This method may provide basically all kinds of cells with sufficient magnetization to allow cell detection by high-resolution magnetic resonance imaging (MRI) and to enable potential magnetic manipulation. In order to efficiently exploit labeled cells, quantify the magnetic load and deliver or follow-up magnetic cells, we herein describe the main requirements that should be applied during the labeling procedure. Moreover we present some recommendations for cell detection and quantification by MRI and detail magnetic guiding on some real-case studies in vitro and in vivo. PMID:24564857

Comparison of pre-processing techniques for fluorescence microscopy images of cells labeled for actin.

PubMed

Muralidhar, Gautam S; Channappayya, Sumohana S; Slater, John H; Blinka, Ellen M; Bovik, Alan C; Frey, Wolfgang; Markey, Mia K

2008-11-06

Automated analysis of fluorescence microscopy images of endothelial cells labeled for actin is important for quantifying changes in the actin cytoskeleton. The current manual approach is laborious and inefficient. The goal of our work is to develop automated image analysis methods, thereby increasing cell analysis throughput. In this study, we present preliminary results on comparing different algorithms for cell segmentation and image denoising.

An information gathering system for medical image inspection

NASA Astrophysics Data System (ADS)

Lee, Young-Jin; Bajcsy, Peter

2005-04-01

We present an information gathering system for medical image inspection that consists of software tools for capturing computer-centric and human-centric information. Computer-centric information includes (1) static annotations, such as (a) image drawings enclosing any selected area, a set of areas with similar colors, a set of salient points, and (b) textual descriptions associated with either image drawings or links between pairs of image drawings, and (2) dynamic (or temporal) information, such as mouse movements, zoom level changes, image panning and frame selections from an image stack. Human-centric information is represented by video and audio signals that are acquired by computer-mounted cameras and microphones. The short-term goal of the presented system is to facilitate learning of medical novices from medical experts, while the long-term goal is to data mine all information about image inspection for assisting in making diagnoses. In this work, we built basic software functionality for gathering computer-centric and human-centric information of the aforementioned variables. Next, we developed the information playback capabilities of all gathered information for educational purposes. Finally, we prototyped text-based and image template-based search engines to retrieve information from recorded annotations, for example, (a) find all annotations containing the word "blood vessels", or (b) search for similar areas to a selected image area. The information gathering system for medical image inspection reported here has been tested with images from the Histology Atlas database.

Deformable Medical Image Registration: A Survey

PubMed Central

Sotiras, Aristeidis; Davatzikos, Christos; Paragios, Nikos

2013-01-01

Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this paper, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this paper is to provide an extensive account of registration techniques in a systematic manner. PMID:23739795

Fast, label-free super-resolution live-cell imaging using rotating coherent scattering (ROCS) microscopy

NASA Astrophysics Data System (ADS)

Jünger, Felix; Olshausen, Philipp V.; Rohrbach, Alexander

2016-07-01

Living cells are highly dynamic systems with cellular structures being often below the optical resolution limit. Super-resolution microscopes, usually based on fluorescence cell labelling, are usually too slow to resolve small, dynamic structures. We present a label-free microscopy technique, which can generate thousands of super-resolved, high contrast images at a frame rate of 100 Hertz and without any post-processing. The technique is based on oblique sample illumination with coherent light, an approach believed to be not applicable in life sciences because of too many interference artefacts. However, by circulating an incident laser beam by 360° during one image acquisition, relevant image information is amplified. By combining total internal reflection illumination with dark-field detection, structures as small as 150 nm become separable through local destructive interferences. The technique images local changes in refractive index through scattered laser light and is applied to living mouse macrophages and helical bacteria revealing unexpected dynamic processes.

Fast, label-free super-resolution live-cell imaging using rotating coherent scattering (ROCS) microscopy

PubMed Central

Jünger, Felix; Olshausen, Philipp v.; Rohrbach, Alexander

2016-01-01

Living cells are highly dynamic systems with cellular structures being often below the optical resolution limit. Super-resolution microscopes, usually based on fluorescence cell labelling, are usually too slow to resolve small, dynamic structures. We present a label-free microscopy technique, which can generate thousands of super-resolved, high contrast images at a frame rate of 100 Hertz and without any post-processing. The technique is based on oblique sample illumination with coherent light, an approach believed to be not applicable in life sciences because of too many interference artefacts. However, by circulating an incident laser beam by 360° during one image acquisition, relevant image information is amplified. By combining total internal reflection illumination with dark-field detection, structures as small as 150 nm become separable through local destructive interferences. The technique images local changes in refractive index through scattered laser light and is applied to living mouse macrophages and helical bacteria revealing unexpected dynamic processes. PMID:27465033

Improved Interactive Medical-Imaging System

NASA Technical Reports Server (NTRS)

Ross, Muriel D.; Twombly, Ian A.; Senger, Steven

2003-01-01

An improved computational-simulation system for interactive medical imaging has been invented. The system displays high-resolution, three-dimensional-appearing images of anatomical objects based on data acquired by such techniques as computed tomography (CT) and magnetic-resonance imaging (MRI). The system enables users to manipulate the data to obtain a variety of views for example, to display cross sections in specified planes or to rotate images about specified axes. Relative to prior such systems, this system offers enhanced capabilities for synthesizing images of surgical cuts and for collaboration by users at multiple, remote computing sites.

Epi-detected quadruple-modal nonlinear optical microscopy for label-free imaging of the tooth

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Zi; Zheng, Wei; Huang, Zhiwei, E-mail: biehzw@nus.edu.sg

2015-01-19

We present an epi-detected quadruple-modal nonlinear optical microscopic imaging technique (i.e., coherent anti-Stokes Raman scattering (CARS), second-harmonic generation (SHG), third-harmonic generation (THG), and two-photon excited fluorescence (TPEF)) based on a picosecond (ps) laser-pumped optical parametric oscillator system for label-free imaging of the tooth. We demonstrate that high contrast ps-CARS images covering both the fingerprint (500–1800 cm{sup −1}) and high-wavenumber (2500–3800 cm{sup −1}) regions can be acquired to uncover the distributions of mineral and organic biomaterials in the tooth, while high quality TPEF, SHG, and THG images of the tooth can also be acquired under ps laser excitation without damaging the samples. Themore » quadruple-modal nonlinear microscopic images (CARS/SHG/THG/TPEF) acquired provide better understanding of morphological structures and biochemical/biomolecular distributions in the dentin, enamel, and the dentin-enamel junction of the tooth without labeling, facilitating optical diagnosis and characterization of the tooth in dentistry.« less

Label-free optical imaging technologies for rapid translation and use during intraoperative surgical and tumor margin assessment

NASA Astrophysics Data System (ADS)

Boppart, Stephen A.; Brown, J. Quincy; Farah, Camile S.; Kho, Esther; Marcu, Laura; Saunders, Christobel M.; Sterenborg, Henricus J. C. M.

2018-02-01

The biannual International Conference on Biophotonics was recently held on April 30 to May 1, 2017, in Fremantle, Western Australia. This continuing conference series brought together key opinion leaders in biophotonics to present their latest results and, importantly, to participate in discussions on the future of the field and what opportunities exist when we collectively work together for using biophotonics for biological discovery and medical applications. One session in this conference, entitled "Tumor Margin Identification: Critiquing Technologies," challenged invited speakers and attendees to review and critique representative label-free optical imaging technologies and their application for intraoperative assessment and guidance in surgical oncology. We are pleased to share a summary in this outlook paper, with the intent to motivate more research inquiry and investigations, to challenge these and other optical imaging modalities to evaluate and improve performance, to spur translation and adoption, and ultimately, to improve the care and outcomes of patients.

Deep learning in the small sample size setting: cascaded feed forward neural networks for medical image segmentation

NASA Astrophysics Data System (ADS)

Gaonkar, Bilwaj; Hovda, David; Martin, Neil; Macyszyn, Luke

2016-03-01

Deep Learning, refers to large set of neural network based algorithms, have emerged as promising machine- learning tools in the general imaging and computer vision domains. Convolutional neural networks (CNNs), a specific class of deep learning algorithms, have been extremely effective in object recognition and localization in natural images. A characteristic feature of CNNs, is the use of a locally connected multi layer topology that is inspired by the animal visual cortex (the most powerful vision system in existence). While CNNs, perform admirably in object identification and localization tasks, typically require training on extremely large datasets. Unfortunately, in medical image analysis, large datasets are either unavailable or are extremely expensive to obtain. Further, the primary tasks in medical imaging are organ identification and segmentation from 3D scans, which are different from the standard computer vision tasks of object recognition. Thus, in order to translate the advantages of deep learning to medical image analysis, there is a need to develop deep network topologies and training methodologies, that are geared towards medical imaging related tasks and can work in a setting where dataset sizes are relatively small. In this paper, we present a technique for stacked supervised training of deep feed forward neural networks for segmenting organs from medical scans. Each `neural network layer' in the stack is trained to identify a sub region of the original image, that contains the organ of interest. By layering several such stacks together a very deep neural network is constructed. Such a network can be used to identify extremely small regions of interest in extremely large images, inspite of a lack of clear contrast in the signal or easily identifiable shape characteristics. What is even more intriguing is that the network stack achieves accurate segmentation even when it is trained on a single image with manually labelled ground truth. We validate

Ontology modularization to improve semantic medical image annotation.

PubMed

Wennerberg, Pinar; Schulz, Klaus; Buitelaar, Paul

2011-02-01

Searching for medical images and patient reports is a significant challenge in a clinical setting. The contents of such documents are often not described in sufficient detail thus making it difficult to utilize the inherent wealth of information contained within them. Semantic image annotation addresses this problem by describing the contents of images and reports using medical ontologies. Medical images and patient reports are then linked to each other through common annotations. Subsequently, search algorithms can more effectively find related sets of documents on the basis of these semantic descriptions. A prerequisite to realizing such a semantic search engine is that the data contained within should have been previously annotated with concepts from medical ontologies. One major challenge in this regard is the size and complexity of medical ontologies as annotation sources. Manual annotation is particularly time consuming labor intensive in a clinical environment. In this article we propose an approach to reducing the size of clinical ontologies for more efficient manual image and text annotation. More precisely, our goal is to identify smaller fragments of a large anatomy ontology that are relevant for annotating medical images from patients suffering from lymphoma. Our work is in the area of ontology modularization, which is a recent and active field of research. We describe our approach, methods and data set in detail and we discuss our results. Copyright © 2010 Elsevier Inc. All rights reserved.

Molecular imaging needles: dual-modality optical coherence tomography and fluorescence imaging of labeled antibodies deep in tissue

PubMed Central

Scolaro, Loretta; Lorenser, Dirk; Madore, Wendy-Julie; Kirk, Rodney W.; Kramer, Anne S.; Yeoh, George C.; Godbout, Nicolas; Sampson, David D.; Boudoux, Caroline; McLaughlin, Robert A.

2015-01-01

Molecular imaging using optical techniques provides insight into disease at the cellular level. In this paper, we report on a novel dual-modality probe capable of performing molecular imaging by combining simultaneous three-dimensional optical coherence tomography (OCT) and two-dimensional fluorescence imaging in a hypodermic needle. The probe, referred to as a molecular imaging (MI) needle, may be inserted tens of millimeters into tissue. The MI needle utilizes double-clad fiber to carry both imaging modalities, and is interfaced to a 1310-nm OCT system and a fluorescence imaging subsystem using an asymmetrical double-clad fiber coupler customized to achieve high fluorescence collection efficiency. We present, to the best of our knowledge, the first dual-modality OCT and fluorescence needle probe with sufficient sensitivity to image fluorescently labeled antibodies. Such probes enable high-resolution molecular imaging deep within tissue. PMID:26137379

A new concept for medical imaging centered on cellular phone technology.

PubMed

Granot, Yair; Ivorra, Antoni; Rubinsky, Boris

2008-04-30

According to World Health Organization reports, some three quarters of the world population does not have access to medical imaging. In addition, in developing countries over 50% of medical equipment that is available is not being used because it is too sophisticated or in disrepair or because the health personnel are not trained to use it. The goal of this study is to introduce and demonstrate the feasibility of a new concept in medical imaging that is centered on cellular phone technology and which may provide a solution to medical imaging in underserved areas. The new system replaces the conventional stand-alone medical imaging device with a new medical imaging system made of two independent components connected through cellular phone technology. The independent units are: a) a data acquisition device (DAD) at a remote patient site that is simple, with limited controls and no image display capability and b) an advanced image reconstruction and hardware control multiserver unit at a central site. The cellular phone technology transmits unprocessed raw data from the patient site DAD and receives and displays the processed image from the central site. (This is different from conventional telemedicine where the image reconstruction and control is at the patient site and telecommunication is used to transmit processed images from the patient site). The primary goal of this study is to demonstrate that the cellular phone technology can function in the proposed mode. The feasibility of the concept is demonstrated using a new frequency division multiplexing electrical impedance tomography system, which we have developed for dynamic medical imaging, as the medical imaging modality. The system is used to image through a cellular phone a simulation of breast cancer tumors in a medical imaging diagnostic mode and to image minimally invasive tissue ablation with irreversible electroporation in a medical imaging interventional mode.

Superparamagnetic iron oxide nanoparticle-labeled cells as an effective vehicle for tracking the GFP gene marker using magnetic resonance imaging

PubMed Central

Zhang, Z; Mascheri, N; Dharmakumar, R; Fan, Z; Paunesku, T; Woloschak, G; Li, D

2010-01-01

Background Detection of a gene using magnetic resonance imaging (MRI) is hindered by the magnetic resonance (MR) targeting gene technique. Therefore it may be advantageous to image gene-expressing cells labeled with superparamagnetic iron oxide (SPIO) nanoparticles by MRI. Methods The GFP-R3230Ac (GFP) cell line was incubated for 24 h using SPIO nanoparticles at a concentration of 20 μg Fe/mL. Cell samples were prepared for iron content analysis and cell function evaluation. The labeled cells were imaged using fluorescent microscopy and MRI. Results SPIO was used to label GFP cells effectively, with no effects on cell function and GFP expression. Iron-loaded GFP cells were successfully imaged with both fluorescent microscopy and T2*-weighted MRI. Prussian blue staining showed intracellular iron accumulation in the cells. All cells were labeled (100% labeling efficiency). The average iron content per cell was 4.75±0.11 pg Fe/cell (P<0.05 versus control). Discussion This study demonstrates that the GFP expression of cells is not altered by the SPIO labeling process. SPIO-labeled GFP cells can be visualized by MRI; therefore, GFP, a gene marker, was tracked indirectly with the SPIO-loaded cells using MRI. The technique holds promise for monitoring the temporal and spatial migration of cells with a gene marker and enhancing the understanding of cell- and gene-based therapeutic strategies. PMID:18956269

Unsupervised Deep Hashing With Pseudo Labels for Scalable Image Retrieval.

PubMed

Zhang, Haofeng; Liu, Li; Long, Yang; Shao, Ling

2018-04-01

In order to achieve efficient similarity searching, hash functions are designed to encode images into low-dimensional binary codes with the constraint that similar features will have a short distance in the projected Hamming space. Recently, deep learning-based methods have become more popular, and outperform traditional non-deep methods. However, without label information, most state-of-the-art unsupervised deep hashing (DH) algorithms suffer from severe performance degradation for unsupervised scenarios. One of the main reasons is that the ad-hoc encoding process cannot properly capture the visual feature distribution. In this paper, we propose a novel unsupervised framework that has two main contributions: 1) we convert the unsupervised DH model into supervised by discovering pseudo labels; 2) the framework unifies likelihood maximization, mutual information maximization, and quantization error minimization so that the pseudo labels can maximumly preserve the distribution of visual features. Extensive experiments on three popular data sets demonstrate the advantages of the proposed method, which leads to significant performance improvement over the state-of-the-art unsupervised hashing algorithms.

The interactive electrode localization utility: software for automatic sorting and labeling of intracranial subdural electrodes

PubMed Central

Tang, Wei; Peled, Noam; Vallejo, Deborah I.; Borzello, Mia; Dougherty, Darin D.; Eskandar, Emad N.; Widge, Alik S.; Cash, Sydney S.; Stufflebeam, Steven M.

2018-01-01

Purpose Existing methods for sorting, labeling, registering, and across-subject localization of electrodes in intracranial encephalography (iEEG) may involve laborious work requiring manual inspection of radiological images. Methods We describe a new open-source software package, the interactive electrode localization utility which presents a full pipeline for the registration, localization, and labeling of iEEG electrodes from CT and MR images. In addition, we describe a method to automatically sort and label electrodes from subdural grids of known geometry. Results We validated our software against manual inspection methods in twelve subjects undergoing iEEG for medically intractable epilepsy. Our algorithm for sorting and labeling performed correct identification on 96% of the electrodes. Conclusions The sorting and labeling methods we describe offer nearly perfect performance and the software package we have distributed may simplify the process of registering, sorting, labeling, and localizing subdural iEEG grid electrodes by manual inspection. PMID:27915398

Labeling of DOTA-conjugated HPMA-based polymers with trivalent metallic radionuclides for molecular imaging.

PubMed

Eppard, Elisabeth; de la Fuente, Ana; Mohr, Nicole; Allmeroth, Mareli; Zentel, Rudolf; Miederer, Matthias; Pektor, Stefanie; Rösch, Frank

2018-02-27

In this work, the in vitro and in vivo stabilities and the pharmacology of HPMA-made homopolymers were studied by means of radiometal-labeled derivatives. Aiming to identify the fewer amount and the optimal DOTA-linker structure that provides quantitative labeling yields, diverse DOTA-linker systems were conjugated in different amounts to HPMA homopolymers to coordinate trivalent radiometals Me(III)* = gallium-68, scandium-44, and lutetium-177. Short linkers and as low as 1.6% DOTA were enough to obtain labeling yields > 90%. Alkoxy linkers generally exhibited lower labeling yields than alkane analogues despite of similar chain length and DOTA incorporation rate. High stability of the radiolabel in all examined solutions was observed for all conjugates. Labeling with scandium-44 allowed for in vivo PET imaging and ex vivo measurements of organ distribution for up to 24 h. This study confirms the principle applicability of DOTA-HPMA conjugates for labeling with different trivalent metallic radionuclides allowing for diagnosis and therapy.

Hello World Deep Learning in Medical Imaging.

PubMed

Lakhani, Paras; Gray, Daniel L; Pett, Carl R; Nagy, Paul; Shih, George

2018-05-03

There is recent popularity in applying machine learning to medical imaging, notably deep learning, which has achieved state-of-the-art performance in image analysis and processing. The rapid adoption of deep learning may be attributed to the availability of machine learning frameworks and libraries to simplify their use. In this tutorial, we provide a high-level overview of how to build a deep neural network for medical image classification, and provide code that can help those new to the field begin their informatics projects.

Simultaneous stimulated Raman scattering and higher harmonic generation imaging for liver disease diagnosis without labeling

NASA Astrophysics Data System (ADS)

Lin, Jian; Wang, Zi; Zheng, Wei; Huang, Zhiwei

2014-02-01

Nonlinear optical microscopy (e.g., higher harmonic (second-/third- harmonic) generation (HHG), simulated Raman scattering (SRS)) has high diagnostic sensitivity and chemical specificity, making it a promising tool for label-free tissue and cell imaging. In this work, we report a development of a simultaneous SRS and HHG imaging technique for characterization of liver disease in a bile-duct-ligation rat-modal. HHG visualizes collagens formation and reveals the cell morphologic changes associated with liver fibrosis; whereas SRS identifies the distributions of hepatic fat cells formed in steatosis liver tissue. This work shows that the co-registration of SRS and HHG images can be an effective means for label-free diagnosis and characterization of liver steatosis/fibrosis at the cellular and molecular levels.

The Pediatric Urinary Tract and Medical Imaging.

PubMed

Penny, Steven M

2016-01-01

The pediatric urinary tract often is assessed with medical imaging. Consequently, it is essential for medical imaging professionals to have a fundamental understanding of pediatric anatomy, physiology, and common pathology of the urinary tract to provide optimal patient care. This article provides an overview of fetal development, pediatric urinary anatomy and physiology, and common diseases and conditions of the pediatric urinary tract.

Use of mobile devices for medical imaging.

PubMed

Hirschorn, David S; Choudhri, Asim F; Shih, George; Kim, Woojin

2014-12-01

Mobile devices have fundamentally changed personal computing, with many people forgoing the desktop and even laptop computer altogether in favor of a smaller, lighter, and cheaper device with a touch screen. Doctors and patients are beginning to expect medical images to be available on these devices for consultative viewing, if not actual diagnosis. However, this raises serious concerns with regard to the ability of existing mobile devices and networks to quickly and securely move these images. Medical images often come in large sets, which can bog down a network if not conveyed in an intelligent manner, and downloaded data on a mobile device are highly vulnerable to a breach of patient confidentiality should that device become lost or stolen. Some degree of regulation is needed to ensure that the software used to view these images allows all relevant medical information to be visible and manipulated in a clinically acceptable manner. There also needs to be a quality control mechanism to ensure that a device's display accurately conveys the image content without loss of contrast detail. Furthermore, not all mobile displays are appropriate for all types of images. The smaller displays of smart phones, for example, are not well suited for viewing entire chest radiographs, no matter how small and numerous the pixels of the display may be. All of these factors should be taken into account when deciding where, when, and how to use mobile devices for the display of medical images. Copyright © 2014 American College of Radiology. Published by Elsevier Inc. All rights reserved.

Direct fluorescent-dye labeling of α-tubulin in mammalian cells for live cell and superresolution imaging.

PubMed

Schvartz, Tomer; Aloush, Noa; Goliand, Inna; Segal, Inbar; Nachmias, Dikla; Arbely, Eyal; Elia, Natalie

2017-10-15

Genetic code expansion and bioorthogonal labeling provide for the first time a way for direct, site-specific labeling of proteins with fluorescent-dyes in live cells. Although the small size and superb photophysical parameters of fluorescent-dyes offer unique advantages for high-resolution microscopy, this approach has yet to be embraced as a tool in live cell imaging. Here we evaluated the feasibility of this approach by applying it for α-tubulin labeling. After a series of calibrations, we site-specifically labeled α-tubulin with silicon rhodamine (SiR) in live mammalian cells in an efficient and robust manner. SiR-labeled tubulin successfully incorporated into endogenous microtubules at high density, enabling video recording of microtubule dynamics in interphase and mitotic cells. Applying this labeling approach to structured illumination microscopy resulted in an increase in resolution, highlighting the advantages in using a smaller, brighter tag. Therefore, using our optimized assay, genetic code expansion provides an attractive tool for labeling proteins with a minimal, bright tag in quantitative high-resolution imaging. © 2017 Schvartz et al. This article is distributed by The American Society for Cell Biology under license from the author(s). Two months after publication it is available to the public under an Attribution–Noncommercial–Share Alike 3.0 Unported Creative Commons License (http://creativecommons.org/licenses/by-nc-sa/3.0).

Neural networks: Application to medical imaging

NASA Technical Reports Server (NTRS)

Clarke, Laurence P.

1994-01-01

The research mission is the development of computer assisted diagnostic (CAD) methods for improved diagnosis of medical images including digital x-ray sensors and tomographic imaging modalities. The CAD algorithms include advanced methods for adaptive nonlinear filters for image noise suppression, hybrid wavelet methods for feature segmentation and enhancement, and high convergence neural networks for feature detection and VLSI implementation of neural networks for real time analysis. Other missions include (1) implementation of CAD methods on hospital based picture archiving computer systems (PACS) and information networks for central and remote diagnosis and (2) collaboration with defense and medical industry, NASA, and federal laboratories in the area of dual use technology conversion from defense or aerospace to medicine.

Statistical fusion of continuous labels: identification of cardiac landmarks

NASA Astrophysics Data System (ADS)

Xing, Fangxu; Soleimanifard, Sahar; Prince, Jerry L.; Landman, Bennett A.

2011-03-01

Image labeling is an essential task for evaluating and analyzing morphometric features in medical imaging data. Labels can be obtained by either human interaction or automated segmentation algorithms. However, both approaches for labeling suffer from inevitable error due to noise and artifact in the acquired data. The Simultaneous Truth And Performance Level Estimation (STAPLE) algorithm was developed to combine multiple rater decisions and simultaneously estimate unobserved true labels as well as each rater's level of performance (i.e., reliability). A generalization of STAPLE for the case of continuous-valued labels has also been proposed. In this paper, we first show that with the proposed Gaussian distribution assumption, this continuous STAPLE formulation yields equivalent likelihoods for the bias parameter, meaning that the bias parameter-one of the key performance indices-is actually indeterminate. We resolve this ambiguity by augmenting the STAPLE expectation maximization formulation to include a priori probabilities on the performance level parameters, which enables simultaneous, meaningful estimation of both the rater bias and variance performance measures. We evaluate and demonstrate the efficacy of this approach in simulations and also through a human rater experiment involving the identification the intersection points of the right ventricle to the left ventricle in CINE cardiac data.

Statistical Fusion of Continuous Labels: Identification of Cardiac Landmarks.

PubMed

Xing, Fangxu; Soleimanifard, Sahar; Prince, Jerry L; Landman, Bennett A

2011-01-01

Image labeling is an essential task for evaluating and analyzing morphometric features in medical imaging data. Labels can be obtained by either human interaction or automated segmentation algorithms. However, both approaches for labeling suffer from inevitable error due to noise and artifact in the acquired data. The Simultaneous Truth And Performance Level Estimation (STAPLE) algorithm was developed to combine multiple rater decisions and simultaneously estimate unobserved true labels as well as each rater's level of performance (i.e., reliability). A generalization of STAPLE for the case of continuous-valued labels has also been proposed. In this paper, we first show that with the proposed Gaussian distribution assumption, this continuous STAPLE formulation yields equivalent likelihoods for the bias parameter, meaning that the bias parameter-one of the key performance indices-is actually indeterminate. We resolve this ambiguity by augmenting the STAPLE expectation maximization formulation to include a priori probabilities on the performance level parameters, which enables simultaneous, meaningful estimation of both the rater bias and variance performance measures. We evaluate and demonstrate the efficacy of this approach in simulations and also through a human rater experiment involving the identification the intersection points of the right ventricle to the left ventricle in CINE cardiac data.

Integrating medical imaging analyses through a high-throughput bundled resource imaging system

NASA Astrophysics Data System (ADS)

Covington, Kelsie; Welch, E. Brian; Jeong, Ha-Kyu; Landman, Bennett A.

2011-03-01

Exploitation of advanced, PACS-centric image analysis and interpretation pipelines provides well-developed storage, retrieval, and archival capabilities along with state-of-the-art data providence, visualization, and clinical collaboration technologies. However, pursuit of integrated medical imaging analysis through a PACS environment can be limiting in terms of the overhead required to validate, evaluate and integrate emerging research technologies. Herein, we address this challenge through presentation of a high-throughput bundled resource imaging system (HUBRIS) as an extension to the Philips Research Imaging Development Environment (PRIDE). HUBRIS enables PACS-connected medical imaging equipment to invoke tools provided by the Java Imaging Science Toolkit (JIST) so that a medical imaging platform (e.g., a magnetic resonance imaging scanner) can pass images and parameters to a server, which communicates with a grid computing facility to invoke the selected algorithms. Generated images are passed back to the server and subsequently to the imaging platform from which the images can be sent to a PACS. JIST makes use of an open application program interface layer so that research technologies can be implemented in any language capable of communicating through a system shell environment (e.g., Matlab, Java, C/C++, Perl, LISP, etc.). As demonstrated in this proof-of-concept approach, HUBRIS enables evaluation and analysis of emerging technologies within well-developed PACS systems with minimal adaptation of research software, which simplifies evaluation of new technologies in clinical research and provides a more convenient use of PACS technology by imaging scientists.

Real-time Image Processing for Microscopy-based Label-free Imaging Flow Cytometry in a Microfluidic Chip.

PubMed

Heo, Young Jin; Lee, Donghyeon; Kang, Junsu; Lee, Keondo; Chung, Wan Kyun

2017-09-14

Imaging flow cytometry (IFC) is an emerging technology that acquires single-cell images at high-throughput for analysis of a cell population. Rich information that comes from high sensitivity and spatial resolution of a single-cell microscopic image is beneficial for single-cell analysis in various biological applications. In this paper, we present a fast image-processing pipeline (R-MOD: Real-time Moving Object Detector) based on deep learning for high-throughput microscopy-based label-free IFC in a microfluidic chip. The R-MOD pipeline acquires all single-cell images of cells in flow, and identifies the acquired images as a real-time process with minimum hardware that consists of a microscope and a high-speed camera. Experiments show that R-MOD has the fast and reliable accuracy (500 fps and 93.3% mAP), and is expected to be used as a powerful tool for biomedical and clinical applications.

NiftyNet: a deep-learning platform for medical imaging.

PubMed

Gibson, Eli; Li, Wenqi; Sudre, Carole; Fidon, Lucas; Shakir, Dzhoshkun I; Wang, Guotai; Eaton-Rosen, Zach; Gray, Robert; Doel, Tom; Hu, Yipeng; Whyntie, Tom; Nachev, Parashkev; Modat, Marc; Barratt, Dean C; Ourselin, Sébastien; Cardoso, M Jorge; Vercauteren, Tom

2018-05-01

Medical image analysis and computer-assisted intervention problems are increasingly being addressed with deep-learning-based solutions. Established deep-learning platforms are flexible but do not provide specific functionality for medical image analysis and adapting them for this domain of application requires substantial implementation effort. Consequently, there has been substantial duplication of effort and incompatible infrastructure developed across many research groups. This work presents the open-source NiftyNet platform for deep learning in medical imaging. The ambition of NiftyNet is to accelerate and simplify the development of these solutions, and to provide a common mechanism for disseminating research outputs for the community to use, adapt and build upon. The NiftyNet infrastructure provides a modular deep-learning pipeline for a range of medical imaging applications including segmentation, regression, image generation and representation learning applications. Components of the NiftyNet pipeline including data loading, data augmentation, network architectures, loss functions and evaluation metrics are tailored to, and take advantage of, the idiosyncracies of medical image analysis and computer-assisted intervention. NiftyNet is built on the TensorFlow framework and supports features such as TensorBoard visualization of 2D and 3D images and computational graphs by default. We present three illustrative medical image analysis applications built using NiftyNet infrastructure: (1) segmentation of multiple abdominal organs from computed tomography; (2) image regression to predict computed tomography attenuation maps from brain magnetic resonance images; and (3) generation of simulated ultrasound images for specified anatomical poses. The NiftyNet infrastructure enables researchers to rapidly develop and distribute deep learning solutions for segmentation, regression, image generation and representation learning applications, or extend the platform to new

[A study on medical image fusion].

PubMed

Zhang, Er-hu; Bian, Zheng-zhong

2002-09-01

Five algorithms with its advantages and disadvantage for medical image fusion are analyzed. Four kinds of quantitative evaluation criteria for the quality of image fusion algorithms are proposed and these will give us some guidance for future research.

An application of digital network technology to medical image management.

PubMed

Chu, W K; Smith, C L; Wobig, R K; Hahn, F A

1997-01-01

With the advent of network technology, there is considerable interest within the medical community to manage the storage and distribution of medical images by digital means. Higher workflow efficiency leading to better patient care is one of the commonly cited outcomes [1,2]. However, due to the size of medical image files and the unique requirements in detail and resolution, medical image management poses special challenges. Storage requirements are usually large, which implies expenses or investment costs make digital networking projects financially out of reach for many clinical institutions. New advances in network technology and telecommunication, in conjunction with the decreasing cost in computer devices, have made digital image management achievable. In our institution, we have recently completed a pilot project to distribute medical images both within the physical confines of the clinical enterprise as well as outside the medical center campus. The design concept and the configuration of a comprehensive digital image network is described in this report.

First performance evaluation of software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine at CT.

PubMed

Scholtz, Jan-Erik; Wichmann, Julian L; Kaup, Moritz; Fischer, Sebastian; Kerl, J Matthias; Lehnert, Thomas; Vogl, Thomas J; Bauer, Ralf W

2015-03-01

To evaluate software for automatic segmentation, labeling and reformation of anatomical aligned axial images of the thoracolumbar spine on CT in terms of accuracy, potential for time savings and workflow improvement. 77 patients (28 women, 49 men, mean age 65.3±14.4 years) with known or suspected spinal disorders (degenerative spine disease n=32; disc herniation n=36; traumatic vertebral fractures n=9) underwent 64-slice MDCT with thin-slab reconstruction. Time for automatic labeling of the thoracolumbar spine and reconstruction of double-angulated axial images of the pathological vertebrae was compared with manually performed reconstruction of anatomical aligned axial images. Reformatted images of both reconstruction methods were assessed by two observers regarding accuracy of symmetric depiction of anatomical structures. In 33 cases double-angulated axial images were created in 1 vertebra, in 28 cases in 2 vertebrae and in 16 cases in 3 vertebrae. Correct automatic labeling was achieved in 72 of 77 patients (93.5%). Errors could be manually corrected in 4 cases. Automatic labeling required 1min in average. In cases where anatomical aligned axial images of 1 vertebra were created, reconstructions made by hand were significantly faster (p<0.05). Automatic reconstruction was time-saving in cases of 2 and more vertebrae (p<0.05). Both reconstruction methods revealed good image quality with excellent inter-observer agreement. The evaluated software for automatic labeling and anatomically aligned, double-angulated axial image reconstruction of the thoracolumbar spine on CT is time-saving when reconstructions of 2 and more vertebrae are performed. Checking results of automatic labeling is necessary to prevent errors in labeling. Copyright © 2014 Elsevier Ireland Ltd. All rights reserved.

Novel medical image enhancement algorithms

NASA Astrophysics Data System (ADS)

Agaian, Sos; McClendon, Stephen A.

2010-01-01

In this paper, we present two novel medical image enhancement algorithms. The first, a global image enhancement algorithm, utilizes an alpha-trimmed mean filter as its backbone to sharpen images. The second algorithm uses a cascaded unsharp masking technique to separate the high frequency components of an image in order for them to be enhanced using a modified adaptive contrast enhancement algorithm. Experimental results from enhancing electron microscopy, radiological, CT scan and MRI scan images, using the MATLAB environment, are then compared to the original images as well as other enhancement methods, such as histogram equalization and two forms of adaptive contrast enhancement. An image processing scheme for electron microscopy images of Purkinje cells will also be implemented and utilized as a comparison tool to evaluate the performance of our algorithm.

Near-infrared emitting fluorescent nanocrystals-labeled natural killer cells as a platform technology for the optical imaging of immunotherapeutic cells-based cancer therapy

NASA Astrophysics Data System (ADS)

Taik Lim, Yong; Cho, Mi Young; Noh, Young-Woock; Chung, Jin Woong; Chung, Bong Hyun

2009-11-01

This study describes the development of near-infrared optical imaging technology for the monitoring of immunotherapeutic cell-based cancer therapy using natural killer (NK) cells labeled with fluorescent nanocrystals. Although NK cell-based immunotherapeutic strategies have drawn interest as potent preclinical or clinical methods of cancer therapy, there are few reports documenting the molecular imaging of NK cell-based cancer therapy, primarily due to the difficulty of labeling of NK cells with imaging probes. Human natural killer cells (NK92MI) were labeled with anti-human CD56 antibody-coated quantum dots (QD705) for fluorescence imaging. FACS analysis showed that the NK92MI cells labeled with anti-human CD56 antibody-coated QD705 have no effect on the cell viability. The effect of anti-human CD56 antibody-coated QD705 labeling on the NK92MI cell function was investigated by measuring interferon gamma (IFN- γ) production and cytolytic activity. Finally, the NK92MI cells labeled with anti-human CD56 antibody-coated QD705 showed a therapeutic effect similar to that of unlabeled NK92MI cells. Images of intratumorally injected NK92MI cells labeled with anti-human CD56 antibody-coated could be acquired using near-infrared optical imaging both in vivo and in vitro. This result demonstrates that the immunotherapeutic cells labeled with fluorescent nanocrystals can be a versatile platform for the effective tracking of injected therapeutic cells using optical imaging technology, which is very important in cell-based cancer therapies.

An open architecture for medical image workstation

NASA Astrophysics Data System (ADS)

Liang, Liang; Hu, Zhiqiang; Wang, Xiangyun

2005-04-01

Dealing with the difficulties of integrating various medical image viewing and processing technologies with a variety of clinical and departmental information systems and, in the meantime, overcoming the performance constraints in transferring and processing large-scale and ever-increasing image data in healthcare enterprise, we design and implement a flexible, usable and high-performance architecture for medical image workstations. This architecture is not developed for radiology only, but for any workstations in any application environments that may need medical image retrieving, viewing, and post-processing. This architecture contains an infrastructure named Memory PACS and different kinds of image applications built on it. The Memory PACS is in charge of image data caching, pre-fetching and management. It provides image applications with a high speed image data access and a very reliable DICOM network I/O. In dealing with the image applications, we use dynamic component technology to separate the performance-constrained modules from the flexibility-constrained modules so that different image viewing or processing technologies can be developed and maintained independently. We also develop a weakly coupled collaboration service, through which these image applications can communicate with each other or with third party applications. We applied this architecture in developing our product line and it works well. In our clinical sites, this architecture is applied not only in Radiology Department, but also in Ultrasonic, Surgery, Clinics, and Consultation Center. Giving that each concerned department has its particular requirements and business routines along with the facts that they all have different image processing technologies and image display devices, our workstations are still able to maintain high performance and high usability.

Iterative deep convolutional encoder-decoder network for medical image segmentation.

PubMed

Jung Uk Kim; Hak Gu Kim; Yong Man Ro

2017-07-01

In this paper, we propose a novel medical image segmentation using iterative deep learning framework. We have combined an iterative learning approach and an encoder-decoder network to improve segmentation results, which enables to precisely localize the regions of interest (ROIs) including complex shapes or detailed textures of medical images in an iterative manner. The proposed iterative deep convolutional encoder-decoder network consists of two main paths: convolutional encoder path and convolutional decoder path with iterative learning. Experimental results show that the proposed iterative deep learning framework is able to yield excellent medical image segmentation performances for various medical images. The effectiveness of the proposed method has been proved by comparing with other state-of-the-art medical image segmentation methods.

Quantitative imaging features: extension of the oncology medical image database

NASA Astrophysics Data System (ADS)

Patel, M. N.; Looney, P. T.; Young, K. C.; Halling-Brown, M. D.

2015-03-01

Radiological imaging is fundamental within the healthcare industry and has become routinely adopted for diagnosis, disease monitoring and treatment planning. With the advent of digital imaging modalities and the rapid growth in both diagnostic and therapeutic imaging, the ability to be able to harness this large influx of data is of paramount importance. The Oncology Medical Image Database (OMI-DB) was created to provide a centralized, fully annotated dataset for research. The database contains both processed and unprocessed images, associated data, and annotations and where applicable expert determined ground truths describing features of interest. Medical imaging provides the ability to detect and localize many changes that are important to determine whether a disease is present or a therapy is effective by depicting alterations in anatomic, physiologic, biochemical or molecular processes. Quantitative imaging features are sensitive, specific, accurate and reproducible imaging measures of these changes. Here, we describe an extension to the OMI-DB whereby a range of imaging features and descriptors are pre-calculated using a high throughput approach. The ability to calculate multiple imaging features and data from the acquired images would be valuable and facilitate further research applications investigating detection, prognosis, and classification. The resultant data store contains more than 10 million quantitative features as well as features derived from CAD predictions. Theses data can be used to build predictive models to aid image classification, treatment response assessment as well as to identify prognostic imaging biomarkers.

Facilitating medical information search using Google Glass connected to a content-based medical image retrieval system.

PubMed

Widmer, Antoine; Schaer, Roger; Markonis, Dimitrios; Muller, Henning

2014-01-01

Wearable computing devices are starting to change the way users interact with computers and the Internet. Among them, Google Glass includes a small screen located in front of the right eye, a camera filming in front of the user and a small computing unit. Google Glass has the advantage to provide online services while allowing the user to perform tasks with his/her hands. These augmented glasses uncover many useful applications, also in the medical domain. For example, Google Glass can easily provide video conference between medical doctors to discuss a live case. Using these glasses can also facilitate medical information search by allowing the access of a large amount of annotated medical cases during a consultation in a non-disruptive fashion for medical staff. In this paper, we developed a Google Glass application able to take a photo and send it to a medical image retrieval system along with keywords in order to retrieve similar cases. As a preliminary assessment of the usability of the application, we tested the application under three conditions (images of the skin; printed CT scans and MRI images; and CT and MRI images acquired directly from an LCD screen) to explore whether using Google Glass affects the accuracy of the results returned by the medical image retrieval system. The preliminary results show that despite minor problems due to the relative stability of the Google Glass, images can be sent to and processed by the medical image retrieval system and similar images are returned to the user, potentially helping in the decision making process.

Digital diagnosis of medical images

NASA Astrophysics Data System (ADS)

Heinonen, Tomi; Kuismin, Raimo; Jormalainen, Raimo; Dastidar, Prasun; Frey, Harry; Eskola, Hannu

2001-08-01

The popularity of digital imaging devices and PACS installations has increased during the last years. Still, images are analyzed and diagnosed using conventional techniques. Our research group begun to study the requirements for digital image diagnostic methods to be applied together with PACS systems. The research was focused on various image analysis procedures (e.g., segmentation, volumetry, 3D visualization, image fusion, anatomic atlas, etc.) that could be useful in medical diagnosis. We have developed Image Analysis software (www.medimag.net) to enable several image-processing applications in medical diagnosis, such as volumetry, multimodal visualization, and 3D visualizations. We have also developed a commercial scalable image archive system (ActaServer, supports DICOM) based on component technology (www.acta.fi), and several telemedicine applications. All the software and systems operate in NT environment and are in clinical use in several hospitals. The analysis software have been applied in clinical work and utilized in numerous patient cases (500 patients). This method has been used in the diagnosis, therapy and follow-up in various diseases of the central nervous system (CNS), respiratory system (RS) and human reproductive system (HRS). In many of these diseases e.g. Systemic Lupus Erythematosus (CNS), nasal airways diseases (RS) and ovarian tumors (HRS), these methods have been used for the first time in clinical work. According to our results, digital diagnosis improves diagnostic capabilities, and together with PACS installations it will become standard tool during the next decade by enabling more accurate diagnosis and patient follow-up.

Live Imaging of Cellular Internalization of Single Colloidal Particle by Combined Label-Free and Fluorescence Total Internal Reflection Microscopy.

PubMed

Byrne, Gerard D; Vllasaliu, Driton; Falcone, Franco H; Somekh, Michael G; Stolnik, Snjezana

2015-11-02

In this work we utilize the combination of label-free total internal reflection microscopy and total internal reflectance fluorescence (TIRM/TIRF) microscopy to achieve a simultaneous, live imaging of single, label-free colloidal particle endocytosis by individual cells. The TIRM arm of the microscope enables label free imaging of the colloid and cell membrane features, while the TIRF arm images the dynamics of fluorescent-labeled clathrin (protein involved in endocytosis via clathrin pathway), expressed in transfected 3T3 fibroblasts cells. Using a model polymeric colloid and cells with a fluorescently tagged clathrin endocytosis pathway, we demonstrate that wide field TIRM/TIRF coimaging enables live visualization of the process of colloidal particle interaction with the labeled cell structure, which is valuable for discerning the membrane events and route of colloid internalization by the cell. We further show that 500 nm in diameter model polystyrene colloid associates with clathrin, prior to and during its cellular internalization. This association is not apparent with larger, 1 μm in diameter colloids, indicating an upper particle size limit for clathrin-mediated endocytosis.

Medical image segmentation based on SLIC superpixels model

NASA Astrophysics Data System (ADS)

Chen, Xiang-ting; Zhang, Fan; Zhang, Ruo-ya

2017-01-01

Medical imaging has been widely used in clinical practice. It is an important basis for medical experts to diagnose the disease. However, medical images have many unstable factors such as complex imaging mechanism, the target displacement will cause constructed defect and the partial volume effect will lead to error and equipment wear, which increases the complexity of subsequent image processing greatly. The segmentation algorithm which based on SLIC (Simple Linear Iterative Clustering, SLIC) superpixels is used to eliminate the influence of constructed defect and noise by means of the feature similarity in the preprocessing stage. At the same time, excellent clustering effect can reduce the complexity of the algorithm extremely, which provides an effective basis for the rapid diagnosis of experts.

Automated processing of label-free Raman microscope images of macrophage cells with standardized regression for high-throughput analysis.

PubMed

Milewski, Robert J; Kumagai, Yutaro; Fujita, Katsumasa; Standley, Daron M; Smith, Nicholas I

2010-11-19

Macrophages represent the front lines of our immune system; they recognize and engulf pathogens or foreign particles thus initiating the immune response. Imaging macrophages presents unique challenges, as most optical techniques require labeling or staining of the cellular compartments in order to resolve organelles, and such stains or labels have the potential to perturb the cell, particularly in cases where incomplete information exists regarding the precise cellular reaction under observation. Label-free imaging techniques such as Raman microscopy are thus valuable tools for studying the transformations that occur in immune cells upon activation, both on the molecular and organelle levels. Due to extremely low signal levels, however, Raman microscopy requires sophisticated image processing techniques for noise reduction and signal extraction. To date, efficient, automated algorithms for resolving sub-cellular features in noisy, multi-dimensional image sets have not been explored extensively. We show that hybrid z-score normalization and standard regression (Z-LSR) can highlight the spectral differences within the cell and provide image contrast dependent on spectral content. In contrast to typical Raman imaging processing methods using multivariate analysis, such as single value decomposition (SVD), our implementation of the Z-LSR method can operate nearly in real-time. In spite of its computational simplicity, Z-LSR can automatically remove background and bias in the signal, improve the resolution of spatially distributed spectral differences and enable sub-cellular features to be resolved in Raman microscopy images of mouse macrophage cells. Significantly, the Z-LSR processed images automatically exhibited subcellular architectures whereas SVD, in general, requires human assistance in selecting the components of interest. The computational efficiency of Z-LSR enables automated resolution of sub-cellular features in large Raman microscopy data sets without

Off-Label Drug Use

MedlinePlus

... their drugs for off-label uses. Off-label marketing is very different from off-label use. Why ... at a higher risk for medication errors, side effects, and unwanted drug reactions. It’s important that the ...

Intra-operative label-free multimodal multiphoton imaging of breast cancer margins and microenvironment (Conference Presentation)

NASA Astrophysics Data System (ADS)

Sun, Yi; You, Sixian; Tu, Haohua; Spillman, Darold R.; Marjanovic, Marina; Chaney, Eric J.; Liu, George Z.; Ray, Partha S.; Higham, Anna; Boppart, Stephen A.

2017-02-01

Label-free multi-photon imaging has been a powerful tool for studying tissue microstructures and biochemical distributions, particularly for investigating tumors and their microenvironments. However, it remains challenging for traditional bench-top multi-photon microscope systems to conduct ex vivo tumor tissue imaging in the operating room due to their bulky setups and laser sources. In this study, we designed, built, and clinically demonstrated a portable multi-modal nonlinear label-free microscope system that combined four modalities, including two- and three- photon fluorescence for studying the distributions of FAD and NADH, and second and third harmonic generation, respectively, for collagen fiber structures and the distribution of micro-vesicles found in tumors and the microenvironment. Optical realignments and switching between modalities were motorized for more rapid and efficient imaging and for a light-tight enclosure, reducing ambient light noise to only 5% within the brightly lit operating room. Using up to 20 mW of laser power after a 20x objective, this system can acquire multi-modal sets of images over 600 μm × 600 μm at an acquisition rate of 60 seconds using galvo-mirror scanning. This portable microscope system was demonstrated in the operating room for imaging fresh, resected, unstained breast tissue specimens, and for assessing tumor margins and the tumor microenvironment. This real-time label-free nonlinear imaging system has the potential to uniquely characterize breast cancer margins and the microenvironment of tumors to intraoperatively identify structural, functional, and molecular changes that could indicate the aggressiveness of the tumor.

Label-free in situ SERS imaging of biofilms.

PubMed

Ivleva, Natalia P; Wagner, Michael; Szkola, Agathe; Horn, Harald; Niessner, Reinhard; Haisch, Christoph

2010-08-12

Surface-enhanced Raman scattering (SERS) is a promising technique for the chemical characterization of biological systems. It yields highly informative spectra, can be applied directly in aqueous environment, and has high sensitivity in comparison with normal Raman spectroscopy. Moreover, SERS imaging can provide chemical information with spatial resolution in the micrometer range (chemical imaging). In this paper, we report for the first time on the application of SERS for in situ, label-free imaging of biofilms and demonstrate the suitability of this technique for the characterization of the complex biomatrix. Biofilms, being communities of microorganisms embedded in a matrix of extracellular polymeric substances (EPS), represent the predominant mode of microbial life. Knowledge of the chemical composition and the structure of the biofilm matrix is important in different fields, e.g., medicine, biology, and industrial processes. We used colloidal silver nanoparticles for the in situ SERS analysis. Good SERS measurement reproducibility, along with a significant enhancement of Raman signals by SERS (>10(4)) and highly informative SERS signature, enables rapid SERS imaging (1 s for a single spectrum) of the biofilm matrix. Altogether, this work illustrates the potential of SERS for biofilm analysis, including the detection of different constituents and the determination of their distribution in a biofilm even at low biomass concentration.

Tumor margin assessment of surgical tissue specimen of cancer patients using label-free hyperspectral imaging

NASA Astrophysics Data System (ADS)

Fei, Baowei; Lu, Guolan; Wang, Xu; Zhang, Hongzheng; Little, James V.; Magliocca, Kelly R.; Chen, Amy Y.

2017-02-01

We are developing label-free hyperspectral imaging (HSI) for tumor margin assessment. HSI data, hypercube (x,y,λ), consists of a series of high-resolution images of the same field of view that are acquired at different wavelengths. Every pixel on the HSI image has an optical spectrum. We developed preprocessing and classification methods for HSI data. We used spectral features from HSI data for the classification of cancer and benign tissue. We collected surgical tissue specimens from 16 human patients who underwent head and neck (H&N) cancer surgery. We acquired both HSI, autofluorescence images, and fluorescence images with 2-NBDG and proflavine from the specimens. Digitized histologic slides were examined by an H&N pathologist. The hyperspectral imaging and classification method was able to distinguish between cancer and normal tissue from oral cavity with an average accuracy of 90+/-8%, sensitivity of 89+/-9%, and specificity of 91+/-6%. For tissue specimens from the thyroid, the method achieved an average accuracy of 94+/-6%, sensitivity of 94+/-6%, and specificity of 95+/-6%. Hyperspectral imaging outperformed autofluorescence imaging or fluorescence imaging with vital dye (2-NBDG or proflavine). This study suggests that label-free hyperspectral imaging has great potential for tumor margin assessment in surgical tissue specimens of H&N cancer patients. Further development of the hyperspectral imaging technology is warranted for its application in image-guided surgery.

Radionuclide and Fluorescence Imaging of Clear Cell Renal Cell Carcinoma Using Dual Labeled Anti-Carbonic Anhydrase IX Antibody G250.

PubMed

Muselaers, Constantijn H J; Rijpkema, Mark; Bos, Desirée L; Langenhuijsen, Johan F; Oyen, Wim J G; Mulders, Peter F A; Oosterwijk, Egbert; Boerman, Otto C

2015-08-01

Tumor targeted optical imaging using antibodies labeled with near infrared fluorophores is a sensitive imaging modality that might be used during surgery to assure complete removal of malignant tissue. We evaluated the feasibility of dual modality imaging and image guided surgery with the dual labeled anti-carbonic anhydrase IX antibody preparation (111)In-DTPA-G250-IRDye800CW in mice with intraperitoneal clear cell renal cell carcinoma. BALB/c nu/nu mice with intraperitoneal SK-RC-52 lesions received 10 μg DTPA-G250-IRDye800CW labeled with 15 MBq (111)In or 10 μg of the dual labeled irrelevant control antibody NUH-82 (20 mice each). To evaluate when tumors could be detected, 4 mice per group were imaged weekly during 5 weeks with single photon emission computerized tomography/computerized tomography and the fluorescence imaging followed by ex vivo biodistribution studies. As early as 1 week after tumor cell inoculation single photon emission computerized tomography and fluorescence images showed clear delineation of intraperitoneal clear cell renal cell carcinoma with good concordance between single photon emission computerized tomography/computerized tomography and fluorescence images. The high and specific accumulation of the dual labeled antibody conjugate in tumors was confirmed in the biodistribution studies. Maximum tumor uptake was observed 1 week after inoculation (mean ± SD 58.5% ± 18.7% vs 5.6% ± 2.3% injected dose per gm for DTPA-G250-IRDye800CW vs NUH-82, respectively). High tumor uptake was also observed at other time points. This study demonstrates the feasibility of dual modality imaging with dual labeled antibody (111)In-DTPA-G250-IRDye800CW in a clear cell renal cell carcinoma model. Results indicate that preoperative and intraoperative detection of carbonic anhydrase IX expressing tumors, positive resection margins and metastasis might be feasible with this approach. Copyright © 2015 American Urological Association Education and Research

Real-time image mosaicing for medical applications.

PubMed

Loewke, Kevin E; Camarillo, David B; Jobst, Christopher A; Salisbury, J Kenneth

2007-01-01

In this paper we describe the development of a robotically-assisted image mosaicing system for medical applications. The processing occurs in real-time due to a fast initial image alignment provided by robotic position sensing. Near-field imaging, defined by relatively large camera motion, requires translations as well as pan and tilt orientations to be measured. To capture these measurements we use 5-d.o.f. sensing along with a hand-eye calibration to account for sensor offset. This sensor-based approach speeds up the mosaicing, eliminates cumulative errors, and readily handles arbitrary camera motions. Our results have produced visually satisfactory mosaics on a dental model but can be extended to other medical images.

Fully convolutional networks with double-label for esophageal cancer image segmentation by self-transfer learning

NASA Astrophysics Data System (ADS)

Xue, Di-Xiu; Zhang, Rong; Zhao, Yuan-Yuan; Xu, Jian-Ming; Wang, Ya-Lei

2017-07-01

Cancer recognition is the prerequisite to determine appropriate treatment. This paper focuses on the semantic segmentation task of microvascular morphological types on narrowband images to aid clinical examination of esophageal cancer. The most challenge for semantic segmentation is incomplete-labeling. Our key insight is to build fully convolutional networks (FCNs) with double-label to make pixel-wise predictions. The roi-label indicating ROIs (region of interest) is introduced as extra constraint to guild feature learning. Trained end-to-end, the FCN model with two target jointly optimizes both segmentation of sem-label (semantic label) and segmentation of roi-label within the framework of self-transfer learning based on multi-task learning theory. The learning representation ability of shared convolutional networks for sem-label is improved with support of roi-label via achieving a better understanding of information outside the ROIs. Our best FCN model gives satisfactory segmentation result with mean IU up to 77.8% (pixel accuracy > 90%). The results show that the proposed approach is able to assist clinical diagnosis to a certain extent.

Multi-channel medical imaging system

DOEpatents

Frangioni, John V

2013-12-31

A medical imaging system provides simultaneous rendering of visible light and fluorescent images. The system may employ dyes in a small-molecule form that remain in the subject's blood stream for several minutes, allowing real-time imaging of the subject's circulatory system superimposed upon a conventional, visible light image of the subject. The system may provide an excitation light source to excite the fluorescent substance and a visible light source for general illumination within the same optical guide used to capture images. The system may be configured for use in open surgical procedures by providing an operating area that is closed to ambient light. The systems described herein provide two or more diagnostic imaging channels for capture of multiple, concurrent diagnostic images and may be used where a visible light image may be usefully supplemented by two or more images that are independently marked for functional interest.

Multi-channel medical imaging system

DOEpatents

Frangioni, John V.

2016-05-03

A medical imaging system provides simultaneous rendering of visible light and fluorescent images. The system may employ dyes in a small-molecule form that remain in a subject's blood stream for several minutes, allowing real-time imaging of the subject's circulatory system superimposed upon a conventional, visible light image of the subject. The system may provide an excitation light source to excite the fluorescent substance and a visible light source for general illumination within the same optical guide used to capture images. The system may be configured for use in open surgical procedures by providing an operating area that is closed to ambient light. The systems described herein provide two or more diagnostic imaging channels for capture of multiple, concurrent diagnostic images and may be used where a visible light image may be usefully supplemented by two or more images that are independently marked for functional interest.

Client-side Medical Image Colorization in a Collaborative Environment.

PubMed

Virag, Ioan; Stoicu-Tivadar, Lăcrămioara; Crişan-Vida, Mihaela

2015-01-01

The paper presents an application related to collaborative medicine using a browser based medical visualization system with focus on the medical image colorization process and the underlying open source web development technologies involved. Browser based systems allow physicians to share medical data with their remotely located counterparts or medical students, assisting them during patient diagnosis, treatment monitoring, surgery planning or for educational purposes. This approach brings forth the advantage of ubiquity. The system can be accessed from a any device, in order to process the images, assuring the independence towards having a specific proprietary operating system. The current work starts with processing of DICOM (Digital Imaging and Communications in Medicine) files and ends with the rendering of the resulting bitmap images on a HTML5 (fifth revision of the HyperText Markup Language) canvas element. The application improves the image visualization emphasizing different tissue densities.

Deep neural ensemble for retinal vessel segmentation in fundus images towards achieving label-free angiography.

PubMed

Lahiri, A; Roy, Abhijit Guha; Sheet, Debdoot; Biswas, Prabir Kumar

2016-08-01

Automated segmentation of retinal blood vessels in label-free fundus images entails a pivotal role in computed aided diagnosis of ophthalmic pathologies, viz., diabetic retinopathy, hypertensive disorders and cardiovascular diseases. The challenge remains active in medical image analysis research due to varied distribution of blood vessels, which manifest variations in their dimensions of physical appearance against a noisy background. In this paper we formulate the segmentation challenge as a classification task. Specifically, we employ unsupervised hierarchical feature learning using ensemble of two level of sparsely trained denoised stacked autoencoder. First level training with bootstrap samples ensures decoupling and second level ensemble formed by different network architectures ensures architectural revision. We show that ensemble training of auto-encoders fosters diversity in learning dictionary of visual kernels for vessel segmentation. SoftMax classifier is used for fine tuning each member autoencoder and multiple strategies are explored for 2-level fusion of ensemble members. On DRIVE dataset, we achieve maximum average accuracy of 95.33% with an impressively low standard deviation of 0.003 and Kappa agreement coefficient of 0.708. Comparison with other major algorithms substantiates the high efficacy of our model.

An intelligent framework for medical image retrieval using MDCT and multi SVM.

PubMed

Balan, J A Alex Rajju; Rajan, S Edward

2014-01-01

Volumes of medical images are rapidly generated in medical field and to manage them effectively has become a great challenge. This paper studies the development of innovative medical image retrieval based on texture features and accuracy. The objective of the paper is to analyze the image retrieval based on diagnosis of healthcare management systems. This paper traces the development of innovative medical image retrieval to estimate both the image texture features and accuracy. The texture features of medical images are extracted using MDCT and multi SVM. Both the theoretical approach and the simulation results revealed interesting observations and they were corroborated using MDCT coefficients and SVM methodology. All attempts to extract the data about the image in response to the query has been computed successfully and perfect image retrieval performance has been obtained. Experimental results on a database of 100 trademark medical images show that an integrated texture feature representation results in 98% of the images being retrieved using MDCT and multi SVM. Thus we have studied a multiclassification technique based on SVM which is prior suitable for medical images. The results show the retrieval accuracy of 98%, 99% for different sets of medical images with respect to the class of image.

PET imaging of osteosarcoma in dogs using a fluorine-18-labeled monoclonal antibody fab fragment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Page, R.L.; Garg, P.K.; Gard, S.

Four dogs with histologically confirmed osteogenic sarcoma were studied with PET following intravenous injection of the {sup 18}F-labeled Fab fragment of TP-3, a monoclonal antibody specific for human and canine osteosarcomas. The antibody fragment was labeled using the N-succinimidyl (8-(4{prime}-({sup 18}F)fluorobenzyl)amino)suberate acylation agent. Blood clearance of activity was biphasic in all dogs but half-times were variable (T{sub 1/2{beta}} = 2-13 hr). Catabolism of labeled Fab was reflected by the decrease in protein-associated activity in serum from more than 90% at 1 min to 60%-80% at 4 hr. PET images demonstrated increased accumulation of {sup 18}F at the primary tumor sitemore » relative to normal contralateral bone in one dog as early as 15 min after injection. Biopsies obtained after euthanasia indicated higher uptake at the edges of the tumor as observed on the PET scans. Tumor uptake was 1-3 x 10{sup -3}% injected dose/g, a level similar to that reported for other Fab fragments in human tumors. In the three dogs with metastatic disease, early PET images reflected activity in the blood pool but later uptake was observed in suspected metastatic sites. These results, although preliminary, suggest that PET imaging of {sup 18}F-labeled antibody fragments is feasible and that dogs with spontaneous tumors could be a valuable model for preclinical research with radioimmunoconjugates. 34 refs., 6 figs., 2 tabs.« less

Machine Learning in Medical Imaging.

PubMed

Giger, Maryellen L

2018-03-01

Advances in both imaging and computers have synergistically led to a rapid rise in the potential use of artificial intelligence in various radiological imaging tasks, such as risk assessment, detection, diagnosis, prognosis, and therapy response, as well as in multi-omics disease discovery. A brief overview of the field is given here, allowing the reader to recognize the terminology, the various subfields, and components of machine learning, as well as the clinical potential. Radiomics, an expansion of computer-aided diagnosis, has been defined as the conversion of images to minable data. The ultimate benefit of quantitative radiomics is to (1) yield predictive image-based phenotypes of disease for precision medicine or (2) yield quantitative image-based phenotypes for data mining with other -omics for discovery (ie, imaging genomics). For deep learning in radiology to succeed, note that well-annotated large data sets are needed since deep networks are complex, computer software and hardware are evolving constantly, and subtle differences in disease states are more difficult to perceive than differences in everyday objects. In the future, machine learning in radiology is expected to have a substantial clinical impact with imaging examinations being routinely obtained in clinical practice, providing an opportunity to improve decision support in medical image interpretation. The term of note is decision support, indicating that computers will augment human decision making, making it more effective and efficient. The clinical impact of having computers in the routine clinical practice may allow radiologists to further integrate their knowledge with their clinical colleagues in other medical specialties and allow for precision medicine. Copyright © 2018. Published by Elsevier Inc.

78 FR 24211 - Draft Guidance for Industry on Safety Considerations for Container Labels and Carton Labeling...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-24

... container labels and carton labeling design, is the second in a series of three planned guidance documents...] Draft Guidance for Industry on Safety Considerations for Container Labels and Carton Labeling Design To... entitled ``Safety Considerations for Container Labels and Carton Labeling Design to Minimize Medication...

Label-free optical imaging of membrane patches for atomic force microscopy

PubMed Central

Churnside, Allison B.; King, Gavin M.; Perkins, Thomas T.

2010-01-01

In atomic force microscopy (AFM), finding sparsely distributed regions of interest can be difficult and time-consuming. Typically, the tip is scanned until the desired object is located. This process can mechanically or chemically degrade the tip, as well as damage fragile biological samples. Protein assemblies can be detected using the back-scattered light from a focused laser beam. We previously used back-scattered light from a pair of laser foci to stabilize an AFM. In the present work, we integrate these techniques to optically image patches of purple membranes prior to AFM investigation. These rapidly acquired optical images were aligned to the subsequent AFM images to ~40 nm, since the tip position was aligned to the optical axis of the imaging laser. Thus, this label-free imaging efficiently locates sparsely distributed protein assemblies for subsequent AFM study while simultaneously minimizing degradation of the tip and the sample. PMID:21164738

Brain medical image diagnosis based on corners with importance-values.

PubMed

Gao, Linlin; Pan, Haiwei; Li, Qing; Xie, Xiaoqin; Zhang, Zhiqiang; Han, Jinming; Zhai, Xiao

2017-11-21

Brain disorders are one of the top causes of human death. Generally, neurologists analyze brain medical images for diagnosis. In the image analysis field, corners are one of the most important features, which makes corner detection and matching studies essential. However, existing corner detection studies do not consider the domain information of brain. This leads to many useless corners and the loss of significant information. Regarding corner matching, the uncertainty and structure of brain are not employed in existing methods. Moreover, most corner matching studies are used for 3D image registration. They are inapplicable for 2D brain image diagnosis because of the different mechanisms. To address these problems, we propose a novel corner-based brain medical image classification method. Specifically, we automatically extract multilayer texture images (MTIs) which embody diagnostic information from neurologists. Moreover, we present a corner matching method utilizing the uncertainty and structure of brain medical images and a bipartite graph model. Finally, we propose a similarity calculation method for diagnosis. Brain CT and MRI image sets are utilized to evaluate the proposed method. First, classifiers are trained in N-fold cross-validation analysis to produce the best θ and K. Then independent brain image sets are tested to evaluate the classifiers. Moreover, the classifiers are also compared with advanced brain image classification studies. For the brain CT image set, the proposed classifier outperforms the comparison methods by at least 8% on accuracy and 2.4% on F1-score. Regarding the brain MRI image set, the proposed classifier is superior to the comparison methods by more than 7.3% on accuracy and 4.9% on F1-score. Results also demonstrate that the proposed method is robust to different intensity ranges of brain medical image. In this study, we develop a robust corner-based brain medical image classifier. Specifically, we propose a corner detection

Large-Scale medical image analytics: Recent methodologies, applications and Future directions.

PubMed

Zhang, Shaoting; Metaxas, Dimitris

2016-10-01

Despite the ever-increasing amount and complexity of annotated medical image data, the development of large-scale medical image analysis algorithms has not kept pace with the need for methods that bridge the semantic gap between images and diagnoses. The goal of this position paper is to discuss and explore innovative and large-scale data science techniques in medical image analytics, which will benefit clinical decision-making and facilitate efficient medical data management. Particularly, we advocate that the scale of image retrieval systems should be significantly increased at which interactive systems can be effective for knowledge discovery in potentially large databases of medical images. For clinical relevance, such systems should return results in real-time, incorporate expert feedback, and be able to cope with the size, quality, and variety of the medical images and their associated metadata for a particular domain. The design, development, and testing of the such framework can significantly impact interactive mining in medical image databases that are growing rapidly in size and complexity and enable novel methods of analysis at much larger scales in an efficient, integrated fashion. Copyright © 2016. Published by Elsevier B.V.

Visualization index for image-enabled medical records

NASA Astrophysics Data System (ADS)

Dong, Wenjie; Zheng, Weilin; Sun, Jianyong; Zhang, Jianguo

2011-03-01

With the widely use of healthcare information technology in hospitals, the patients' medical records are more and more complex. To transform the text- or image-based medical information into easily understandable and acceptable form for human, we designed and developed an innovation indexing method which can be used to assign an anatomical 3D structure object to every patient visually to store indexes of the patients' basic information, historical examined image information and RIS report information. When a doctor wants to review patient historical records, he or she can first load the anatomical structure object and the view the 3D index of this object using a digital human model tool kit. This prototype system helps doctors to easily and visually obtain the complete historical healthcare status of patients, including large amounts of medical data, and quickly locate detailed information, including both reports and images, from medical information systems. In this way, doctors can save time that may be better used to understand information, obtain a more comprehensive understanding of their patients' situations, and provide better healthcare services to patients.

[An improved medical image fusion algorithm and quality evaluation].

PubMed

Chen, Meiling; Tao, Ling; Qian, Zhiyu

2009-08-01

Medical image fusion is of very important value for application in medical image analysis and diagnosis. In this paper, the conventional method of wavelet fusion is improved,so a new algorithm of medical image fusion is presented and the high frequency and low frequency coefficients are studied respectively. When high frequency coefficients are chosen, the regional edge intensities of each sub-image are calculated to realize adaptive fusion. The choice of low frequency coefficient is based on the edges of images, so that the fused image preserves all useful information and appears more distinctly. We apply the conventional and the improved fusion algorithms based on wavelet transform to fuse two images of human body and also evaluate the fusion results through a quality evaluation method. Experimental results show that this algorithm can effectively retain the details of information on original images and enhance their edge and texture features. This new algorithm is better than the conventional fusion algorithm based on wavelet transform.

Label-Free Raman Imaging to Monitor Breast Tumor Signatures

NASA Astrophysics Data System (ADS)

Ciubuc, John

Methods built on Raman spectroscopy have shown major potential in describing and discriminating between malignant and benign specimens. Accurate, real-time medical diagnosis benefits in substantial improvements through this vibrational optical method. Not only is acquisition of data possible in milliseconds and analysis in minutes, Raman allows concurrent detection and monitoring of all biological components. Besides validating a significant Raman signature distinction between non-tumorigenic (MCF-10A) and tumorigenic (MCF-7) breast epithelial cells, this study reveals a label-free method to assess overexpression of epidermal growth factor receptors (EGFR) in tumor cells. EGFR overexpression sires Raman features associated with phosphorylated threonine and serine, and modifications of DNA/RNA characteristics. Investigations by gel electrophoresis reveal EGF induction of phosphorylated Akt, agreeing with the Raman results. The analysis presented is a vital step toward Raman-based evaluation of EGF receptors in breast cancer cells. With the goal of clinically applying Raman-guided methods for diagnosis of breast tumors, the current results lay the basis for proving label-free optical alternatives in making prognosis of the disease.

Label-free in vivo flow cytometry in zebrafish using two-photon autofluorescence imaging.

PubMed

Zeng, Yan; Xu, Jin; Li, Dong; Li, Li; Wen, Zilong; Qu, Jianan Y

2012-07-01

We demonstrate a label-free in vivo flow cytometry in zebrafish blood vessels based on two-photon excited autofluorescence imaging. The major discovery in this work is the strong autofluorescence emission from the plasma in zebrafish blood. The plasma autofluorescence provides excellent contrast for visualizing blood vessels and counting blood cells. In addition, the cellular nicotinamide adenine dinucleotide autofluorescence enables in vivo imaging and counting of white blood cells (neutrophils).

Decreased sensitivity of early imaging with In-111 oxine-labeled leukocytes in detection of occult infection: concise communication

DOE Office of Scientific and Technical Information (OSTI.GOV)

Datz, F.L.; Jacobs, J.; Baker, W.

1984-03-01

Imaging with leukocytes labeled with indium-111 oxine is a sensitive technique for detecting sites of occult infection. Traditionally, imaging is performed 24 hr after injection. The authors undertook a prospective study of 35 patients (40 studies) with possible occult infection to see whether a 24-hr delay in imaging is really necessary. Patients were imaged at 1-4 hr and again at 24 hr after injection. The early images had a sensitivity of only 33%, compared with 95% for the 24-hr images. Of the seven studies that were positive on both early and delayed images, 71% had more intense uptake at 24more » hr. There were no false-positive early images. It was concluded that imaging 1-4 hr after injection with In-111 oxine-labeled leukocytes has a low sensitivity for detecting occult infection. However, a positive early image is specific for a site of infection.« less

A survey of medical image registration - under review.

PubMed

Viergever, Max A; Maintz, J B Antoine; Klein, Stefan; Murphy, Keelin; Staring, Marius; Pluim, Josien P W

2016-10-01

A retrospective view on the past two decades of the field of medical image registration is presented, guided by the article "A survey of medical image registration" (Maintz and Viergever, 1998). It shows that the classification of the field introduced in that article is still usable, although some modifications to do justice to advances in the field would be due. The main changes over the last twenty years are the shift from extrinsic to intrinsic registration, the primacy of intensity-based registration, the breakthrough of nonlinear registration, the progress of inter-subject registration, and the availability of generic image registration software packages. Two problems that were called urgent already 20 years ago, are even more urgent nowadays: Validation of registration methods, and translation of results of image registration research to clinical practice. It may be concluded that the field of medical image registration has evolved, but still is in need of further development in various aspects. Copyright © 2016 Elsevier B.V. All rights reserved.

[Application of medical imaging to general thoracic surgery].

PubMed

Oizumi, Hiroyuki

2014-07-01

Medical imaging technology is rapidly progressing. Positron emission tomography (PET) has played major role in the staging and choice of treatment modality in lung cancer patients. Magnetic resonance imaging (MRI) is now routinely used for mediastinal tumors and the use of diffusion-weighted images (DWI) may help in the diagnosis of malignancies including lung cancers. The benefits of medical imaging technology are not limited to diagnostics, and include simulation or navigation for complex lung resection and other procedures. Multidetector row computed tomography (MDCT) shortens imaging time to obtain detailed and precise volume data, which improves diagnosis of small-sized lung cancers. 3-dimensional reconstruction of the volume data allows the safe performance of thoracoscopic surgery. For lung lobectomy, identification of the branching structures, diameter, and length of the arteries is useful in selecting the procedure for blood vessel treatment. For lung segmentectomy, visualization of venous branches in the affected segments and intersegmental veins has facilitated the preoperative determination of the anatomical intersegmental plane. Therefore, the application of medical imaging technology is useful in general thoracic surgery.

Adapting smartphones for low-cost optical medical imaging

NASA Astrophysics Data System (ADS)

Pratavieira, Sebastião.; Vollet-Filho, José D.; Carbinatto, Fernanda M.; Blanco, Kate; Inada, Natalia M.; Bagnato, Vanderlei S.; Kurachi, Cristina

2015-06-01

Optical images have been used in several medical situations to improve diagnosis of lesions or to monitor treatments. However, most systems employ expensive scientific (CCD or CMOS) cameras and need computers to display and save the images, usually resulting in a high final cost for the system. Additionally, this sort of apparatus operation usually becomes more complex, requiring more and more specialized technical knowledge from the operator. Currently, the number of people using smartphone-like devices with built-in high quality cameras is increasing, which might allow using such devices as an efficient, lower cost, portable imaging system for medical applications. Thus, we aim to develop methods of adaptation of those devices to optical medical imaging techniques, such as fluorescence. Particularly, smartphones covers were adapted to connect a smartphone-like device to widefield fluorescence imaging systems. These systems were used to detect lesions in different tissues, such as cervix and mouth/throat mucosa, and to monitor ALA-induced protoporphyrin-IX formation for photodynamic treatment of Cervical Intraepithelial Neoplasia. This approach may contribute significantly to low-cost, portable and simple clinical optical imaging collection.

Medical imaging by fluorescent x-ray CT: its preliminary clinical evaluation

NASA Astrophysics Data System (ADS)

Takeda, Tohoru; Zeniya, Tsutomu; Wu, Jin; Yu, Quanwen; Lwin, Thet T.; Tsuchiya, Yoshinori; Rao, Donepudi V.; Yuasa, Tetsuya; Yashiro, Toru; Dilmanian, F. Avraham; Itai, Yuji; Akatsuka, Takao

2002-01-01

Fluorescent x-ray CT (FXCT) with synchrotron radiation (SR) is being developed to detect the very low concentration of specific elements. The endogenous iodine of the human thyroid and the non-radioactive iodine labeled BMIPP in myocardium were imaged by FXCT. FXCT system consists of a silicon (111) double crystal monochromator, an x-ray slit, a scanning table for object positioning, a fluorescent x-ray detector, and a transmission x-ray detector. Monochromatic x-ray with 37 keV energy was collimated into a pencil beam (from 1 mm to 0.025 mm). FXCT clearly imaged endogenous iodine of thyroid and iodine labeled BMIPP in myocardium, whereas transmission x-ray CT could not demonstrate iodine. The distribution of iodine was heterogeneous within thyroid cancer, and its concentration was lower than that of normal thyroid. Distribution of BMIPP in normal rat myocardium was almost homogeneous; however, reduced uptake was slightly shown in ischemic region. FXCT is a highly sensitive imaging modality to detect very low concentration of specific element and will be applied to reveal endogenous iodine distribution in thyroid and to use tracer study with various kinds of labeled material.

Automatic atlas-based three-label cartilage segmentation from MR knee images

PubMed Central

Shan, Liang; Zach, Christopher; Charles, Cecil; Niethammer, Marc

2016-01-01

Osteoarthritis (OA) is the most common form of joint disease and often characterized by cartilage changes. Accurate quantitative methods are needed to rapidly screen large image databases to assess changes in cartilage morphology. We therefore propose a new automatic atlas-based cartilage segmentation method for future automatic OA studies. Atlas-based segmentation methods have been demonstrated to be robust and accurate in brain imaging and therefore also hold high promise to allow for reliable and high-quality segmentations of cartilage. Nevertheless, atlas-based methods have not been well explored for cartilage segmentation. A particular challenge is the thinness of cartilage, its relatively small volume in comparison to surrounding tissue and the difficulty to locate cartilage interfaces – for example the interface between femoral and tibial cartilage. This paper focuses on the segmentation of femoral and tibial cartilage, proposing a multi-atlas segmentation strategy with non-local patch-based label fusion which can robustly identify candidate regions of cartilage. This method is combined with a novel three-label segmentation method which guarantees the spatial separation of femoral and tibial cartilage, and ensures spatial regularity while preserving the thin cartilage shape through anisotropic regularization. Our segmentation energy is convex and therefore guarantees globally optimal solutions. We perform an extensive validation of the proposed method on 706 images of the Pfizer Longitudinal Study. Our validation includes comparisons of different atlas segmentation strategies, different local classifiers, and different types of regularizers. To compare to other cartilage segmentation approaches we validate based on the 50 images of the SKI10 dataset. PMID:25128683

Color transfer algorithm in medical images

NASA Astrophysics Data System (ADS)

Wang, Weihong; Xu, Yangfa

2007-12-01

In digital virtual human project, image data acquires from the freezing slice of human body specimen. The color and brightness between a group of images of a certain organ could be quite different. The quality of these images could bring great difficulty in edge extraction, segmentation, as well as 3D reconstruction process. Thus it is necessary to unify the color of the images. The color transfer algorithm is a good algorithm to deal with this kind of problem. This paper introduces the principle of this algorithm and uses it in the medical image processing.

The Handbook of Medical Image Perception and Techniques

NASA Astrophysics Data System (ADS)

Samei, Ehsan; Krupinski, Elizabeth

2014-07-01

1. Medical image perception Ehsan Samei and Elizabeth Krupinski; Part I. Historical Reflections and Theoretical Foundations: 2. A short history of image perception in medical radiology Harold Kundel and Calvin Nodine; 3. Spatial vision research without noise Arthur Burgess; 4. Signal detection theory, a brief history Arthur Burgess; 5. Signal detection in radiology Arthur Burgess; 6. Lessons from dinners with the giants of modern image science Robert Wagner; Part II. Science of Image Perception: 7. Perceptual factors in reading medical images Elizabeth Krupinski; 8. Cognitive factors in reading medical images David Manning; 9. Satisfaction of search in traditional radiographic imaging Kevin Berbaum, Edmund Franken, Robert Caldwell and Kevin Schartz; 10. The role of expertise in radiologic image interpretation Calvin Nodine and Claudia Mello-Thoms; 11. A primer of image quality and its perceptual relevance Robert Saunders and Ehsan Samei; 12. Beyond the limitations of human vision Maria Petrou; Part III. Perception Metrology: 13. Logistical issues in designing perception experiments Ehsan Samei and Xiang Li; 14. ROC analysis: basic concepts and practical applications Georgia Tourassi; 15. Multi-reader ROC Steve Hillis; 16. Recent developments in FROC methodology Dev Chakraborty; 17. Observer models as a surrogate to perception experiments Craig Abbey and Miguel Eckstein; 18. Implementation of observer models Matthew Kupinski; Part IV. Decision Support and Computer Aided Detection: 19. CAD: an image perception perspective Maryellen Giger and Weijie Chen; 20. Common designs of CAD studies Yulei Jiang; 21. Perceptual effect of CAD in reading chest images Matthew Freedman and Teresa Osicka; 22. Perceptual issues in mammography and CAD Michael Ulissey; 23. How perceptual factors affect the use and accuracy of CAD for interpretation of CT images Ronald Summers; 24. CAD: risks and benefits for radiologists' decisions Eugenio Alberdi, Andrey Povyakalo, Lorenzo Strigini and

The Handbook of Medical Image Perception and Techniques

NASA Astrophysics Data System (ADS)

Samei, Ehsan; Krupinski, Elizabeth

2009-12-01

1. Medical image perception Ehsan Samei and Elizabeth Krupinski; Part I. Historical Reflections and Theoretical Foundations: 2. A short history of image perception in medical radiology Harold Kundel and Calvin Nodine; 3. Spatial vision research without noise Arthur Burgess; 4. Signal detection theory, a brief history Arthur Burgess; 5. Signal detection in radiology Arthur Burgess; 6. Lessons from dinners with the giants of modern image science Robert Wagner; Part II. Science of Image Perception: 7. Perceptual factors in reading medical images Elizabeth Krupinski; 8. Cognitive factors in reading medical images David Manning; 9. Satisfaction of search in traditional radiographic imaging Kevin Berbaum, Edmund Franken, Robert Caldwell and Kevin Schartz; 10. The role of expertise in radiologic image interpretation Calvin Nodine and Claudia Mello-Thoms; 11. A primer of image quality and its perceptual relevance Robert Saunders and Ehsan Samei; 12. Beyond the limitations of human vision Maria Petrou; Part III. Perception Metrology: 13. Logistical issues in designing perception experiments Ehsan Samei and Xiang Li; 14. ROC analysis: basic concepts and practical applications Georgia Tourassi; 15. Multi-reader ROC Steve Hillis; 16. Recent developments in FROC methodology Dev Chakraborty; 17. Observer models as a surrogate to perception experiments Craig Abbey and Miguel Eckstein; 18. Implementation of observer models Matthew Kupinski; Part IV. Decision Support and Computer Aided Detection: 19. CAD: an image perception perspective Maryellen Giger and Weijie Chen; 20. Common designs of CAD studies Yulei Jiang; 21. Perceptual effect of CAD in reading chest images Matthew Freedman and Teresa Osicka; 22. Perceptual issues in mammography and CAD Michael Ulissey; 23. How perceptual factors affect the use and accuracy of CAD for interpretation of CT images Ronald Summers; 24. CAD: risks and benefits for radiologists' decisions Eugenio Alberdi, Andrey Povyakalo, Lorenzo Strigini and

Identifying regions of interest in medical images using self-organizing maps.

PubMed

Teng, Wei-Guang; Chang, Ping-Lin

2012-10-01

Advances in data acquisition, processing and visualization techniques have had a tremendous impact on medical imaging in recent years. However, the interpretation of medical images is still almost always performed by radiologists. Developments in artificial intelligence and image processing have shown the increasingly great potential of computer-aided diagnosis (CAD). Nevertheless, it has remained challenging to develop a general approach to process various commonly used types of medical images (e.g., X-ray, MRI, and ultrasound images). To facilitate diagnosis, we recommend the use of image segmentation to discover regions of interest (ROI) using self-organizing maps (SOM). We devise a two-stage SOM approach that can be used to precisely identify the dominant colors of a medical image and then segment it into several small regions. In addition, by appropriately conducting the recursive merging steps to merge smaller regions into larger ones, radiologists can usually identify one or more ROIs within a medical image.

Comparison of indium-labeled-leukocyte imaging with sequential technetium-gallium scanning in the diagnosis of low-grade musculoskeletal sepsis. A prospective study

DOE Office of Scientific and Technical Information (OSTI.GOV)

Merkel, K.D.; Brown, M.L.; Dewanjee, M.K.

We prospectively compared sequential technetium-gallium imaging with indium-labeled-leukocyte imaging in fifty patients with suspected low-grade musculoskeletal sepsis. Adequate images and follow-up examinations were obtained for forty-two patients. The presence or absence of low-grade sepsis was confirmed by histological and bacteriological examinations of tissue specimens taken at surgery in thirty of the forty-two patients. In these thirty patients, the sensitivity of sequential Tc-Ga imaging was 48 per cent, the specificity was 86 per cent, and the accuracy was 57 per cent, whereas the sensitivity of the indium-labeled-leukocyte technique was 83 per cent, the specificity was 86 per cent, and the accuracymore » was 83 per cent. When the additional twelve patients for whom surgery was deemed unnecessary were considered, the sensitivity of sequential Tc-Ga imaging was 50 per cent, the specificity was 78 per cent, and the accuracy was 62 per cent, as compared with a sensitivity of 83 per cent, a specificity of 94 per cent, and an accuracy of 88 per cent with the indium-labeled-leukocyte method. In patients with a prosthesis the indium-labeled-leukocyte image was 94 per cent accurate, compared with 75 per cent accuracy for sequential Tc-Ga imaging. Statistical analysis of these data demonstrated that the indium-labeled-leukocyte technique was superior to sequential Tc-Ga imaging in detecting areas of low-grade musculoskeletal sepsis.« less

Large-scale Labeled Datasets to Fuel Earth Science Deep Learning Applications

NASA Astrophysics Data System (ADS)

Maskey, M.; Ramachandran, R.; Miller, J.

2017-12-01

Deep learning has revolutionized computer vision and natural language processing with various algorithms scaled using high-performance computing. However, generic large-scale labeled datasets such as the ImageNet are the fuel that drives the impressive accuracy of deep learning results. Large-scale labeled datasets already exist in domains such as medical science, but creating them in the Earth science domain is a challenge. While there are ways to apply deep learning using limited labeled datasets, there is a need in the Earth sciences for creating large-scale labeled datasets for benchmarking and scaling deep learning applications. At the NASA Marshall Space Flight Center, we are using deep learning for a variety of Earth science applications where we have encountered the need for large-scale labeled datasets. We will discuss our approaches for creating such datasets and why these datasets are just as valuable as deep learning algorithms. We will also describe successful usage of these large-scale labeled datasets with our deep learning based applications.

Wavelet optimization for content-based image retrieval in medical databases.

PubMed

Quellec, G; Lamard, M; Cazuguel, G; Cochener, B; Roux, C

2010-04-01

We propose in this article a content-based image retrieval (CBIR) method for diagnosis aid in medical fields. In the proposed system, images are indexed in a generic fashion, without extracting domain-specific features: a signature is built for each image from its wavelet transform. These image signatures characterize the distribution of wavelet coefficients in each subband of the decomposition. A distance measure is then defined to compare two image signatures and thus retrieve the most similar images in a database when a query image is submitted by a physician. To retrieve relevant images from a medical database, the signatures and the distance measure must be related to the medical interpretation of images. As a consequence, we introduce several degrees of freedom in the system so that it can be tuned to any pathology and image modality. In particular, we propose to adapt the wavelet basis, within the lifting scheme framework, and to use a custom decomposition scheme. Weights are also introduced between subbands. All these parameters are tuned by an optimization procedure, using the medical grading of each image in the database to define a performance measure. The system is assessed on two medical image databases: one for diabetic retinopathy follow up and one for screening mammography, as well as a general purpose database. Results are promising: a mean precision of 56.50%, 70.91% and 96.10% is achieved for these three databases, when five images are returned by the system. Copyright 2009 Elsevier B.V. All rights reserved.

Multiplex and label-free screening of foodborne pathogens using surface plasmon resonance imaging

USDA-ARS?s Scientific Manuscript database

In order to protect outbreaks caused by foodborne pathogens, more rapid and efficient methods are needed for pathogen screening from food samples. Surface plasmon resonance imaging (SPRi) is an emerging optical technique, which allows for label-free screening of multiple targets simultaneously with ...

Optimized labeling of membrane proteins for applications to super-resolution imaging in confined cellular environments using monomeric streptavidin.

PubMed

Chamma, Ingrid; Rossier, Olivier; Giannone, Grégory; Thoumine, Olivier; Sainlos, Matthieu

2017-04-01

Recent progress in super-resolution imaging (SRI) has created a strong need to improve protein labeling with probes of small size that minimize the target-to-label distance, increase labeling density, and efficiently penetrate thick biological tissues. This protocol describes a method for labeling genetically modified proteins incorporating a small biotin acceptor peptide with a 3-nm fluorescent probe, monomeric streptavidin. We show how to express, purify, and conjugate the probe to organic dyes with different fluorescent properties, and how to label selectively biotinylated membrane proteins for SRI techniques (point accumulation in nanoscale topography (PAINT), stimulated emission depletion (STED), stochastic optical reconstruction microscopy (STORM)). This method is complementary to the previously described anti-GFP-nanobody/SNAP-tag strategies, with the main advantage being that it requires only a short 15-amino-acid tag, and can thus be used with proteins resistant to fusion with large tags and for multicolor imaging. The protocol requires standard molecular biology/biochemistry equipment, making it easily accessible for laboratories with only basic skills in cell biology and biochemistry. The production/purification/conjugation steps take ∼5 d, and labeling takes a few minutes to an hour.