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1.
Evidence Supporting LI-RADS Major Features for CT- and MR Imaging-based Diagnosis of Hepatocellular Carcinoma: A Systematic Review.
Tang, A, Bashir, MR, Corwin, MT, Cruite, I, Dietrich, CF, Do, RKG, Ehman, EC, Fowler, KJ, Hussain, HK, Jha, RC, et al
Radiology. 2018;(1):29-48
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Abstract
The Liver Imaging Reporting and Data System (LI-RADS) standardizes the interpretation, reporting, and data collection for imaging examinations in patients at risk for hepatocellular carcinoma (HCC). It assigns category codes reflecting relative probability of HCC to imaging-detected liver observations based on major and ancillary imaging features. LI-RADS also includes imaging features suggesting malignancy other than HCC. Supported and endorsed by the American College of Radiology (ACR), the system has been developed by a committee of radiologists, hepatologists, pathologists, surgeons, lexicon experts, and ACR staff, with input from the American Association for the Study of Liver Diseases and the Organ Procurement Transplantation Network/United Network for Organ Sharing. Development of LI-RADS has been based on literature review, expert opinion, rounds of testing and iteration, and feedback from users. This article summarizes and assesses the quality of evidence supporting each LI-RADS major feature for diagnosis of HCC, as well as of the LI-RADS imaging features suggesting malignancy other than HCC. Based on the evidence, recommendations are provided for or against their continued inclusion in LI-RADS. © RSNA, 2017 Online supplemental material is available for this article.
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Diabetic macular edema grading in retinal images using vector quantization and semi-supervised learning.
Ren, F, Cao, P, Zhao, D, Wan, C
Technology and health care : official journal of the European Society for Engineering and Medicine. 2018;(S1):389-397
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Abstract
BACKGROUND Diabetic macular edema (DME) is one of the severe complication of diabetic retinopathy causing severe vision loss and leads to blindness in severe cases if left untreated. OBJECTIVE To grade the severity of DME in retinal images. METHODS Firstly, the macular is localized using its anatomical features and the information of the macula location with respect to the optic disc. Secondly, a novel method for the exudates detection is proposed. The possible exudate regions are segmented using vector quantization technique and formulated using a set of feature vectors. A semi-supervised learning with graph based classifier is employed to identify the true exudates. Thirdly, the disease severity is graded into different stages based on the location of exudates and the macula coordinates. RESULTS The results are obtained with the mean value of 0.975 and 0.942 for accuracy and F1-scrore, respectively. CONCLUSION The present work contributes to macula localization, exudate candidate identification with vector quantization and exudate candidate classification with semi-supervised learning. The proposed method and the state-of-the-art approaches are compared in terms of performance, and experimental results show the proposed system overcomes the challenge of the DME grading and demonstrate a promising effectiveness.
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Early detection of cardiac allograft vasculopathy using highly automated 3-dimensional optical coherence tomography analysis.
Pazdernik, M, Chen, Z, Bedanova, H, Kautzner, J, Melenovsky, V, Karmazin, V, Malek, I, Tomasek, A, Ozabalova, E, Krejci, J, et al
The Journal of heart and lung transplantation : the official publication of the International Society for Heart Transplantation. 2018;(8):992-1000
Abstract
BACKGROUND Optical coherence tomography (OCT)-based studies of cardiac allograft vasculopathy (CAV) published thus far have focused mainly on frame-based qualitative analysis of the vascular wall. Full capabilities of this inherently 3-dimensional (3D) imaging modality to quantify CAV have not been fully exploited. METHODS Coronary OCT imaging was performed at 1 month and 12 months after heart transplant (HTx) during routine surveillance cardiac catheterization. Both baseline and follow-up OCT examinations were analyzed using proprietary, highly automated 3D graph-based optimal segmentation software. Automatically identified borders were efficiently adjudicated using our "just-enough-interaction" graph-based segmentation approach that allows to efficiently correct local and regional segmentation errors without slice-by-slice retracing of borders. RESULTS A total of 50 patients with paired baseline and follow-up OCT studies were included. After registration of baseline and follow-up pullbacks, a total of 356 ± 89 frames were analyzed per patient. During the first post-transplant year, significant reduction in the mean luminal area (p = 0.028) and progression in mean intimal thickness (p = 0.001) were observed. Proximal parts of imaged coronary arteries were affected more than distal parts (p < 0.001). High levels of LDL cholesterol (p = 0.02) and total cholesterol (p = 0.031) in the first month after HTx were the main factors associated with early CAV development. CONCLUSIONS Our novel, highly automated 3D OCT image analysis method for analyzing intimal and medial thickness in HTx recipients provides fast, accurate, and highly detailed quantitative data on early CAV changes, which are characterized by significant luminal reduction and intimal thickness progression as early as within the first 12 months after HTx.
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Semiautomated Evaluation of the Primary Motor Cortex in Patients with Amyotrophic Lateral Sclerosis at 3T.
Donatelli, G, Retico, A, Caldarazzo Ienco, E, Cecchi, P, Costagli, M, Frosini, D, Biagi, L, Tosetti, M, Siciliano, G, Cosottini, M
AJNR. American journal of neuroradiology. 2018;(1):63-69
Abstract
BACKGROUND AND PURPOSE Amyotrophic lateral sclerosis is a neurodegenerative disease involving the upper and lower motor neurons. In amyotrophic lateral sclerosis, pathologic changes in the primary motor cortex include Betz cell depletion and the presence of reactive iron-loaded microglia, detectable on 7T MR images as atrophy and T2*-hypointensity. Our purposes were the following: 1) to investigate the signal hypointensity-to-thickness ratio of the primary motor cortex as a radiologic marker of upper motor neuron involvement in amyotrophic lateral sclerosis with a semiautomated method at 3T, 2) to compare 3T and 7T results, and 3) to evaluate whether semiautomated measurement outperforms visual image assessment. MATERIALS AND METHODS We investigated 27 patients and 13 healthy subjects at 3T, and 19 patients and 18 healthy subjects at 7T, performing a high-resolution 3D multiecho T2*-weighted sequence targeting the primary motor cortex. The signal hypointensity-to-thickness ratio of the primary motor cortex was calculated with a semiautomated method depicting signal intensity profiles of the cortex. Images were also visually classified as "pathologic" or "nonpathologic" based on the primary motor cortex signal intensity and thickness. RESULTS The signal hypointensity-to-thickness ratio of the primary motor cortex was greater in patients than in controls (P < .001), and it correlated with upper motor neuron impairment in patients (ρ = 0.57, P < .001). The diagnostic accuracy of the signal hypointensity-to-thickness ratio was high at 3T (area under the curve = 0.89) and even higher at 7T (area under the curve = 0.94). The sensitivity of the semiautomated method (0.81) outperformed the sensitivity of the visual assessment (0.56-0.63) at 3T. CONCLUSIONS The signal hypointensity-to-thickness ratio of the primary motor cortex calculated with a semiautomated method is suggested as a radiologic marker of upper motor neuron burden in patients with amyotrophic lateral sclerosis. This semiautomated method may be useful for improving the subjective radiologic evaluation of upper motor neuron pathology in patients suspected of having amyotrophic lateral sclerosis.
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Retinal Microaneurysms Detection Using Local Convergence Index Features.
Dashtbozorg, B, Zhang, J, Huang, F, Ter Haar Romeny, BM
IEEE transactions on image processing : a publication of the IEEE Signal Processing Society. 2018;(7):3300-3315
Abstract
Retinal microaneurysms (MAs) are the earliest clinical sign of diabetic retinopathy disease. Detection of MAs is crucial for the early diagnosis of diabetic retinopathy and prevention of blindness. In this paper, a novel and reliable method for automatic detection of MAs in retinal images is proposed. In the first stage of the proposed method, several preliminary microaneurysm candidates are extracted using a gradient weighting technique and an iterative thresholding approach. In the next stage, in addition to intensity and shape descriptors, a new set of features based on local convergence index filters is extracted for each candidate. Finally, the collective set of features is fed to a hybrid sampling/boosting classifier to discriminate the MAs from non-MAs candidates. The method is evaluated on images with different resolutions and modalities (color and scanning laser ophthalmoscope) using six publicly available data sets including the retinopathy online challenges (ROC) data set. The proposed method achieves an average sensitivity score of 0.471 on the ROC data set outperforming state-of-the-art approaches in an extensive comparison. The experimental results on the other five data sets demonstrate the effectiveness and robustness of the proposed MAs detection method regardless of different image resolutions and modalities.
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Scan, dwell, decide: Strategies for detecting abnormalities in diabetic retinopathy.
Rangrej, SB, Sivaswamy, J, Srivastava, P
PloS one. 2018;(11):e0207086
Abstract
Diabetic retinopathy (DR) is a disease which is widely diagnosed using (colour fundus) images. Efficiency and accuracy are critical in diagnosing DR as lack of timely intervention can lead to irreversible visual impairment. In this paper, we examine strategies for scrutinizing images which affect diagnostic performance of medical practitioners via an eye-tracking study. A total of 56 subjects with 0 to 18 years of experience participated in the study. Every subject was asked to detect DR from 40 images. The findings indicate that practitioners use mainly two types of strategies characterized by either higher dwell duration or longer track length. The main findings of the study are that higher dwell-based strategy led to higher average accuracy (> 85%) in diagnosis, irrespective of the expertise of practitioner; whereas, the average obtained accuracy with a long-track length-based strategy was dependent on the expertise of the practitioner. In the second part of the paper, we use the experimental findings to recommend a scanning strategy for fast and accurate diagnosis of DR that can be potentially used by image readers. This is derived by combining the eye-tracking gaze maps of medical experts in a novel manner based on a set of rules. This strategy requires scrutiny of images in a manner which is consistent with spatial preferences found in human perception in general and in the domain of fundus images in particular. The Levenshtein distance-based assessment of gaze patterns also establish the effectiveness of the derived scanning pattern and is thus recommended for image readers.
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Automatic detection of microaneurysms in retinal fundus images.
Wu, B, Zhu, W, Shi, F, Zhu, S, Chen, X
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 2017;:106-112
Abstract
Diabetic retinopathy (DR) is one of the leading causes of new cases of blindness. Early and accurate detection of microaneurysms (MAs) is important for diagnosis and grading of diabetic retinopathy. In this paper, a new method for the automatic detection of MAs in eye fundus images is proposed. The proposed method consists of four main steps: preprocessing, candidate extraction, feature extraction and classification. A total of 27 characteristic features which contain local features and profile features are extracted for KNN classifier to distinguish true MAs from spurious candidates. The proposed method has been evaluated on two public database: ROC and e-optha. The experimental result demonstrates the efficiency and effectiveness of the proposed method, and it has the potential to be used to diagnose DR clinically.
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A location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images.
Liu, Q, Zou, B, Chen, J, Ke, W, Yue, K, Chen, Z, Zhao, G
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society. 2017;:78-86
Abstract
The automatic exudate segmentation in colour retinal fundus images is an important task in computer aided diagnosis and screening systems for diabetic retinopathy. In this paper, we present a location-to-segmentation strategy for automatic exudate segmentation in colour retinal fundus images, which includes three stages: anatomic structure removal, exudate location and exudate segmentation. In anatomic structure removal stage, matched filters based main vessels segmentation method and a saliency based optic disk segmentation method are proposed. The main vessel and optic disk are then removed to eliminate the adverse affects that they bring to the second stage. In the location stage, we learn a random forest classifier to classify patches into two classes: exudate patches and exudate-free patches, in which the histograms of completed local binary patterns are extracted to describe the texture structures of the patches. Finally, the local variance, the size prior about the exudate regions and the local contrast prior are used to segment the exudate regions out from patches which are classified as exudate patches in the location stage. We evaluate our method both at exudate-level and image-level. For exudate-level evaluation, we test our method on e-ophtha EX dataset, which provides pixel level annotation from the specialists. The experimental results show that our method achieves 76% in sensitivity and 75% in positive prediction value (PPV), which both outperform the state of the art methods significantly. For image-level evaluation, we test our method on DiaRetDB1, and achieve competitive performance compared to the state of the art methods.
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Multiparametric or practical quantitative liver MRI: towards millisecond, fat fraction, kilopascal and function era.
Unal, E, Idilman, IS, Karçaaltıncaba, M
Expert review of gastroenterology & hepatology. 2017;(2):167-182
Abstract
New advances in liver magnetic resonance imaging (MRI) may enable diagnosis of unseen pathologies by conventional techniques. Normal T1 (550-620 ms for 1.5 T and 700-850 ms for 3 T), T2, T2* (>20 ms), T1rho (40-50 ms) mapping, proton density fat fraction (PDFF) (≤5%) and stiffness (2-3kPa) values can enable differentiation of a normal liver from chronic liver and diffuse diseases. Gd-EOB-DTPA can enable assessment of liver function by using postcontrast hepatobiliary phase or T1 reduction rate (normally above 60%). T1 mapping can be important for the assessment of fibrosis, amyloidosis and copper overload. T1rho mapping is promising for the assessment of liver collagen deposition. PDFF can allow objective treatment assessment in NAFLD and NASH patients. T2 and T2* are used for iron overload determination. MR fingerprinting may enable single slice acquisition and easy implementation of multiparametric MRI and follow-up of patients. Areas covered: T1, T2, T2*, PDFF and stiffness, diffusion weighted imaging, intravoxel incoherent motion imaging (ADC, D, D* and f values) and function analysis are reviewed. Expert commentary: Multiparametric MRI can enable biopsyless diagnosis and more objective staging of diffuse liver disease, cirrhosis and predisposing diseases. A comprehensive approach is needed to understand and overcome the effects of iron, fat, fibrosis, edema, inflammation and copper on MR relaxometry values in diffuse liver disease.
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In vivo estimation of gamma-aminobutyric acid levels in the neonatal brain.
Tomiyasu, M, Aida, N, Shibasaki, J, Umeda, M, Murata, K, Heberlein, K, Brown, MA, Shimizu, E, Tsuji, H, Obata, T
NMR in biomedicine. 2017;(1)
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Abstract
Gamma-aminobutyric acid (GABA) is the major inhibitory neurotransmitter in the brain, and plays a key role in brain development. However, the in vivo levels of brain GABA in early life are unknown. Using edited MRS, in vivo GABA can be detected as GABA+ signal with contamination of macromolecule signals. GABA+ is evaluated as the peak ratio of GABA+/reference compound, for which creatine (Cr) or water is typically used. However, the concentrations and T1 and T2 relaxation times of these references change during development. Thus, the peak ratio comparison between neonates and children may be inaccurate. The aim of this study was to measure in vivo neonatal brain GABA+ levels, and to investigate the dependency of GABA levels on brain region and age. The basal ganglia and cerebellum of 38 neonates and 12 children were measured using GABA-edited MRS. Two different approaches were used to obtain GABA+ levels: (i) multiplying the GABA/water ratio by the water concentration; and (ii) multiplying the GABA+/Cr by the Cr concentration. Neonates exhibited significantly lower GABA+ levels compared with children in both regions, regardless of the approach employed, consistent with previous ex vivo data. A similar finding of lower GABA+/water and GABA+/Cr in neonates compared with children was observed, except for GABA+/Cr in the cerebellum. This contrasting finding resulted from significantly lower Cr concentrations in the neonate cerebellum, which were approximately 52% of those of children. In conclusion, care should be taken to consider Cr concentrations when comparing GABA+/Cr levels between different-aged subjects.