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Knee cartilage and bone segmentation is critical for physicians to analyze and diagnose articular damage and knee osteoarthritis (OA). Deep learning (DL) methods for medical image segmentation have largely outperformed traditional methods, but they often need large amounts of annotated data for model training, which is very costly and time-consuming for medical experts, especially on 3D images. In this paper, we report a new knee cartilage and bone segmentation framework, KCB-Net, for 3D MR images based on sparse annotation. KCB-Net selects a small subset of slices from 3D images for annotation, and seeks to bridge the performance gap between sparse annotation and full annotation. Specifically, it first identifies a subset of the most effective and representative slices with an unsupervised scheme; it then trains an ensemble model using the annotated slices; next, it self-trains the model using 3D images containing pseudo-labels generated by the ensemble method and improved by a bi-directional hierarchical earth mover's distance (bi-HEMD) algorithm; finally, it fine-tunes the segmentation results using the primal-dual Internal Point Method (IPM). Experiments on four 3D MR knee joint datasets (the SKI10 dataset, OAI ZIB dataset, Iowa dataset, and iMorphics dataset) show that our new framework outperforms state-of-the-art methods on full annotation, and yields high quality results for small annotation ratios even as low as 10%., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background: Imaging-based characteristics associated with the progression of stable coronary atherosclerotic lesions are poorly defined. Utilizing a combination of optical coherence tomography (OCT) and intravascular ultrasound (IVUS) imaging, we aimed to characterize the lesions prone to progression through clinical validation of a semiautomated OCT computational program., Methods: Patients with stable coronary artery disease underwent nonculprit vessel imaging with IVUS and OCT at baseline and IVUS at the 12-month follow-up. After coregistration of baseline and follow-up IVUS images, paired 5-mm segments from each patient were identified, demonstrating the greatest plaque progression and regression as measured by the change in plaque burden. Experienced readers identified plaque features on corresponding baseline OCT segments, and predictors of plaque progression were assessed by multivariable analysis. Each segment then underwent volumetric assessment of the fibrous cap (FC) using proprietary software., Results: Among 23 patients (70% men; median age, 67 years), experienced-reader analysis demonstrated that for every 100 μm increase in mean FC thickness, plaques were 87% less likely to progress ( P = .01), which persisted on multivariable analysis controlling for baseline plaque burden ( P = .05). Automated FC analysis ( n = 17 paired segments) confirmed this finding ( P = .01) and found thinner minimal FC thickness ( P = .01) and larger FC surface area of <65 μm ( P = .02) and <100 μm ( P = .04) in progressing segments than in regressing segments. No additional imaging features predicted plaque progression., Conclusions: A semiautomated FC analysis tool confirmed the significant association between thinner FC and stable coronary plaque progression along entire vessel segments, illustrating the diffuse nature of FC thinning and suggesting a future clinical role in predicting the progression of stable coronary artery disease., Competing Interests: Declaration of competing interest Dr Lopez is a consultant for Abbott-St Jude, runs educational training programs related to optical coherence tomography, and is an investigator in clinical trials with optical coherence tomography. Drs Kassis, Kovarnik, Chen, Weber, Martin, Darki, Woo, Wahle, and Sonka reported no financial interests.

Accurate 3D segmentation of calf muscle compartments in volumetric MR images is essential to diagnose as well as assess progression of muscular diseases. Recently, good segmentation performance was achieved using state-of-the-art deep learning approaches, which, however, require large amounts of annotated data for training. Considering that obtaining sufficiently large medical image annotation datasets is often difficult, time-consuming, and requires expert knowledge, minimizing the necessary sizes of expert-annotated training datasets is of great importance. This paper reports CMC-Net, a new deep learning framework for calf muscle compartment segmentation in 3D MR images that selects an effective small subset of 2D slices from the 3D images to be labelled, while also utilizing unannotated slices to facilitate proper generalization of the subsequent training steps. Our model consists of three parts: (1) an unsupervised method to select the most representative 2D slices on which expert annotation is performed; (2) ensemble model training employing these annotated as well as additional unannotated 2D slices; (3) a model-tuning method using pseudo-labels generated by the ensemble model that results in a trained deep network capable of accurate 3D segmentations. Experiments on segmentation of calf muscle compartments in 3D MR images show that our new approach achieves good performance with very small annotation ratios, and when utilizing full annotation, it outperforms state-of-the-art full annotation segmentation methods. Additional experiments on a 3D MR thigh dataset further verify the ability of our method in segmenting leg muscle groups with sparse annotation., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Background and Purpose: We explored the feasibility of automated, arterial input function independent, vendor neutral prediction of core infarct, and penumbral tissue using complete and partial computed tomographic perfusion data sets through neural networks., Methods: Using retrospective computed tomographic perfusion data from 57 patients, split as training/validation (60%/40%), we developed and validated separate 2-dimensional U-net models for cerebral blood flow (CBF) and time to maximum (Tmax) maps calculation to predict core infarct and tissue at risk, respectively. Once trained, the full sets of 28 input images were sequentially reduced to equitemporal 14, 10, and 7 time points. The averaged structural similarity index measure between the model-derived images and ground truth perfusion maps was compared. Volumes for core infarct and Tmax were compared using the Pearson correlation coefficient., Results: Both CBF and Tmax maps derived using 28 and 14 time points had similar structural similarity index measure (0.80-0.81; P >0.05) when compared with ground truth images. The Pearson correlation for the CBF and Tmax volumes derived from the model using 28-tp with ground truth volumes derived from the RAPID software was 0.69 for CBF and 0.74 for Tmax. The predicted maps were fully concordant in terms of laterality to the commercial perfusion maps. The mean Dice scores were 0.54 for the core infarct and 0.63 for the hypoperfusion maps., Conclusions: Artificial intelligence model-derived volumes show good correlation with RAPID-derived volumes for CBF and Tmax. Within the constraints of a small sample size, the perfusion map quality is similar when using 14-tp instead of 28-tp. Our findings provide proof of concept that vendor neutral artificial intelligence models for computed tomographic perfusion processing using complete or partial image data sets appear feasible. The model accuracy could be further optimized using larger data sets.

Background: During development or regeneration, neurons extend processes (i.e., neurites) via mechanisms that can be readily analyzed in culture. However, defining the impact of a drug or genetic manipulation on such mechanisms can be challenging due to the complex arborization and heterogeneous patterns of neurite growth in vitro. New Method: NeuriteNet is a Convolutional Neural Network (CNN) sorting model that uses a novel adaptation of the XRAI saliency map overlay, which is a region-based attribution method. NeuriteNet compares neuronal populations based on differences in neurite growth patterns, sorts them into respective groups, and overlays a saliency map indicating which areas differentiated the image for the sorting procedure., Results: In this study, we demonstrate that NeuriteNet effectively sorts images corresponding to dissociated neurons into control and treatment groups according to known morphological differences. Furthermore, the saliency map overlay highlights the distinguishing features of the neuron when sorting the images into treatment groups. NeuriteNet also identifies novel morphological differences in neurons cultured from control and genetically modified mouse strains. Comparison with Existing Methods: Unlike other neurite analysis platforms, NeuriteNet does not require manual manipulations, such as segmentation of neurites prior to analysis, and is more accurate than experienced researchers for categorizing neurons according to their pattern of neurite growth., Conclusions: NeuriteNet can be used to effectively screen for morphological differences in a heterogeneous group of neurons and to provide feedback on the key features distinguishing those groups via the saliency map overlay., (Copyright © 2021. Published by Elsevier B.V.)

Objectives: Despite the robust diagnostic performance of MRI-based radiomic features for differentiating between glioblastoma (GBM) and primary central nervous system lymphoma (PCNSL) reported on prior studies, the best sequence or a combination of sequences and model performance across various machine learning pipelines remain undefined. Herein, we compare the diagnostic performance of multiple radiomics-based models to differentiate GBM from PCNSL., Methods: Our retrospective study included 94 patients (34 with PCNSL and 60 with GBM). Model performance was assessed using various MRI sequences across 45 possible model and feature selection combinations for nine different sequence permutations. Predictive performance was assessed using fivefold repeated cross-validation with five repeats. The best and worst performing models were compared to assess differences in performance., Results: The predictive performance, both using individual and a combination of sequences, was fairly robust across multiple top performing models (AUC: 0.961-0.977) but did show considerable variation between the best and worst performing models. The top performing individual sequences had comparable performance to multiparametric models. The best prediction model in our study used a combination of ADC, FLAIR, and T1-CE achieving the highest AUC of 0.977, while the second ranked model used T1-CE and ADC, achieving a cross-validated AUC of 0.975., Conclusion: Radiomics-based predictive accuracy can vary considerably, based on the model and feature selection methods as well as the combination of sequences used. Also, models derived from limited sequences show performance comparable to those derived from all five sequences., Key Points: • Radiomics-based diagnostic performance of various machine learning models for differentiating glioblastoma and PCNSL varies considerably. • ML models using limited or multiple MRI sequences can provide comparable performance, based on the chosen model. • Embedded feature selection models perform better than models using a priori feature reduction., (© 2021. European Society of Radiology.)

Background: Changes in choroidal thickness are associated with various ocular diseases, and the choroid can be imaged using spectral-domain optical coherence tomography (SD-OCT) and enhanced depth imaging OCT (EDI-OCT)., New Method: Eighty macular SD-OCT volumes from 80 patients were obtained using the Zeiss Cirrus machine. Eleven additional control subjects had two Cirrus scans done in one visit along with enhanced depth imaging (EDI-OCT) using the Heidelberg Spectralis machine. To automatically segment choroidal layers from the OCT volumes, our graph-theoretic approach was utilized. The segmentation results were compared with reference standards from two independent graders, and the accuracy of automated segmentation was calculated using unsigned/signed border positioning/thickness errors and Dice similarity coefficient (DSC). The repeatability and reproducibility of our choroidal thicknesses were determined by intraclass correlation coefficient (ICC), coefficient of variation (CV), and repeatability coefficient (RC)., Results: The mean unsigned/signed border positioning errors for the choroidal inner and outer surfaces are 3.39 ± 1.26 µm (mean ± standard deviation)/- 1.52 ± 1.63 µm and 16.09 ± 6.21 µm/4.73 ± 9.53 µm, respectively. The mean unsigned/signed choroidal thickness errors are 16.54 ± 6.47 µm/6.25 ± 9.91 µm, and the mean DSC is 0.949 ± 0.025. The ICC (95% confidence interval), CV, RC values are 0.991 (0.977-0.997), 2.48%, 14.25 µm for the repeatability and 0.991 (0.977-0.997), 2.49%, 14.30 µm for the reproducibility studies, respectively., Comparison With Existing Method(s): The proposed method outperformed our previous method using choroidal vessel segmentation and inter-grader variability., Conclusions: This automated segmentation method can reliably measure choroidal thickness using different OCT platforms., (Copyright © 2021 Elsevier B.V. All rights reserved.)

Prior radiomics studies have focused on two-class brain tumor classification, which limits generalizability. The performance of radiomics in differentiating the three most common malignant brain tumors (glioblastoma (GBM), primary central nervous system lymphoma (PCNSL), and metastatic disease) is assessed; factors affecting the model performance and usefulness of a single sequence versus multiparametric MRI (MP-MRI) remain largely unaddressed. This retrospective study included 253 patients (120 metastatic (lung and brain), 40 PCNSL, and 93 GBM). Radiomic features were extracted for whole a tumor mask (enhancing plus necrotic) and an edema mask (first pipeline), as well as for separate enhancing and necrotic and edema masks (second pipeline). Model performance was evaluated using MP-MRI, individual sequences, and the T1 contrast enhanced (T1-CE) sequence without the edema mask across 45 model/feature selection combinations. The second pipeline showed significantly high performance across all combinations (Brier score: 0.311-0.325). GBRM fit using the full feature set from the T1-CE sequence was the best model. The majority of the top models were built using a full feature set and inbuilt feature selection. No significant difference was seen between the top-performing models for MP-MRI (AUC 0.910) and T1-CE sequence with (AUC 0.908) and without edema masks (AUC 0.894). T1-CE is the single best sequence with comparable performance to that of multiparametric MRI (MP-MRI). Model performance varies based on tumor subregion and the combination of model/feature selection methods.

Few studies have addressed radiomics based differentiation of Glioblastoma (GBM) and intracranial metastatic disease (IMD). However, the effect of different tumor masks, comparison of single versus multiparametric MRI (mp-MRI) or select combination of sequences remains undefined. We cross-compared multiple radiomics based machine learning (ML) models using mp-MRI to determine optimized configurations. Our retrospective study included 60 GBM and 60 IMD patients. Forty-five combinations of ML models and feature reduction strategies were assessed for features extracted from whole tumor and edema masks using mp-MRI [T1W, T2W, T1-contrast enhanced (T1-CE), ADC, FLAIR], individual MRI sequences and combined T1-CE and FLAIR sequences. Model performance was assessed using receiver operating characteristic curve. For mp-MRI, the best model was LASSO model fit using full feature set (AUC 0.953). FLAIR was the best individual sequence (LASSO-full feature set, AUC 0.951). For combined T1-CE/FLAIR sequence, adaBoost-full feature set was the best performer (AUC 0.951). No significant difference was seen between top models across all scenarios, including models using FLAIR only, mp-MRI and combined T1-CE/FLAIR sequence. Top features were extracted from both the whole tumor and edema masks. Shape sphericity is an important discriminating feature.

Background: Quantitative muscle MRI as a sensitive marker of early muscle pathology and disease progression in adult-onset myotonic dystrophy type 1. The utility of muscle MRI as a marker of muscle pathology and disease progression in adult-onset myotonic dystrophy type 1 (DM1) was evaluated., Methods: This prospective, longitudinal study included 67 observations from 36 DM1 patients (50% female), and 92 observations from 49 healthy adults (49% female). Lower-leg 3T magnetic resonance imaging (MRI) scans were acquired. Volume and fat fraction (FF) were estimated using a three-point Dixon method, and T2-relaxometry was determined using a multi-echo spin-echo sequence. Muscles were segmented automatically. Mixed linear models were conducted to determine group differences across muscles and image modality, accounting for age, sex, and repeated observations. Differences in rate of change in volume, T2-relaxometry, and FF were also determined with mixed linear regression that included a group by elapsed time interaction., Results: Compared with healthy adults, DM1 patients exhibited reduced volume of the tibialis anterior, soleus, and gastrocnemius (GAS) (all, P < .05). T2-relaxometry and FF were increased across all calf muscles in DM1 compared to controls. (all, P < .01). Signs of muscle pathology, including reduced volume, and increased T2-relaxometry and FF were already noted in DM1 patients who did not exhibit clinical motor symptoms of DM1. As a group, DM1 patients exhibited a more rapid change than did controls in tibialis posterior volume (P = .05) and GAS T2-relaxometry (P = .03) and FF (P = .06)., Conclusions: Muscle MRI renders sensitive, early markers of muscle pathology and disease progression in DM1. T2 relaxometry may be particularly sensitive to early muscle changes related to DM1., (© 2021 Wiley Periodicals LLC.)

Introduction: Recent studies suggested potential positive correlations between HLA-specific antibodies and development of cardiac allograft vasculopathy (CAV)., Methods: This prospective two-center study investigated early progression of CAV by coronary optical coherence tomography in 1 month and 12 months after heart transplantation (HTx) in 104 patients. Detection and characterization of donor specific (DSA) and MHC class-I polypeptide-related sequence A (MICA) antibodies were performed before, 1, 6 and 12 months after transplantation., Results: During the first post-HTx year, we observed a significant reduction in the mean coronary luminal area (P < .001), and progression in mean intimal thickness (IT) (P < .001). DSA and anti-MICA occurred in 17% of all patients, but no significant relationship was observed between presence of DSA/anti-MICA and IT progression within 12 months after HTx. In contrast, we observed significant association between presence of DSA (p=0.031), de-novo DSA (p=0.031), HLA Class II DSA (p=0.017) and media thickness (MT) progression., Conclusion: Results of our study did not identify a direct association between presence of DSA/anti-MICA and intimal thickness progression in an early period after HTx. However, we found significant relationships between DSA and media thickness progression that may identify a newly recognized immune-pathological aspect of CAV., (Copyright © 2020 Elsevier B.V. All rights reserved.)

Background: An increasing number of patients are receiving left ventricle assist devices as a bridge to heart transplantation. The aim of this study was to determine the difference between patients who received transplants from a left ventricle assist device and those who underwent heart transplantation without a prior left ventricle assist device implantation., Material and Methods: The study included patients who underwent heart transplantation in our institute between January 2010 and November 2018. The following clinical variables were evaluated: donor characteristics, patient's pre-transplant demographical data, post-transplant data, and patient survival. Cardiac allograft vasculopathy progression was prospectively examined (after 1 month and 12 months after heart transplantation) by coronary optical coherence tomography. We were interested in the difference in 1- and 5-year survival between the left ventricle assist device and non-left ventricle assist device groups., Results: A total of 248 patients were identified; out of them, 48 patients received a left ventricle assist device before heart transplantation, whereas 200 had transplants with no prior left ventricle assist device implantation. There were no significant differences in any donor characteristics. The mean duration of cardiopulmonary bypass time in the non-left ventricle assist device group was 156 versus 175 min in the left ventricle assist device group ( p  = 0.009), blood loss was 650 versus 1045 mL ( p  < 0.001), the need to implant an extracorporeal membrane oxygenation was 10% versus 23% ( p  = 0.02). There was no difference in cardiac allograft vasculopathy progression between the groups 1 year after heart transplantation ( p  = 0.528). The 1- and 5-year survival, according to Kaplan-Meier, was 80% and 70% in the left ventricle assist device group, compared to 80% and 73%, respectively, in the non-left ventricle assist device group (Log-rank test: p  = 0.945)., Conclusion: Our results indicate that patients undergoing heart transplantation from left ventricle assist devices suffer significantly more from intraoperative and post-operative complications; however, only insignificant cardiac allograft vasculopathy progression and survival differences between the two groups were observed.

The novel corona-virus disease (COVID-19) pandemic has caused a major outbreak in more than 200 countries around the world, leading to a severe impact on the health and life of many people globally. By October 2020, more than 44 million people were infected, and more than 1,000,000 deaths were reported. Computed Tomography (CT) images can be used as an alternative to the time-consuming RT-PCR test, to detect COVID-19. In this work we propose a segmentation framework to detect chest regions in CT images, which are infected by COVID-19. An architecture similar to a Unet model was employed to detect ground glass regions on a voxel level. As the infected regions tend to form connected components (rather than randomly distributed voxels), a suitable regularization term based on 2D-anisotropic total-variation was developed and added to the loss function. The proposed model is therefore called "TV-Unet". Experimental results obtained on a relatively large-scale CT segmentation dataset of around 900 images, incorporating this new regularization term leads to a 2% gain on overall segmentation performance compared to the Unet trained from scratch. Our experimental analysis, ranging from visual evaluation of the predicted segmentation results to quantitative assessment of segmentation performance (precision, recall, Dice score, and mIoU) demonstrated great ability to identify COVID-19 associated regions of the lungs, achieving a mIoU rate of over 99%, and a Dice score of around 86%., (© 2021 The Authors.)

Automated segmentation of individual calf muscle compartments from 3D magnetic resonance (MR) images is essential for developing quantitative biomarkers for muscular disease progression and its prediction. Achieving clinically acceptable results is a challenging task due to large variations in muscle shape and MR appearance. In this paper, we present a novel fully convolutional network (FCN) that utilizes contextual information in a large neighborhood and embeds edge-aware constraints for individual calf muscle compartment segmentations. An encoder-decoder architecture is used to systematically enlarge convolution receptive field and preserve information at all resolutions. Edge positions derived from the FCN output muscle probability maps are explicitly regularized using kernel-based edge detection in an end-to-end optimization framework. Our method was evaluated on 40 T1-weighted MR images of 10 healthy and 30 diseased subjects by fourfold cross-validation. Mean DICE coefficients of 88.00-91.29% and mean absolute surface positioning errors of 1.04-1.66 mm were achieved for the five 3D muscle compartments., (Copyright © 2020 Elsevier Ltd. All rights reserved.)

The COVID-19 pandemic is causing a major outbreak in more than 150 countries around the world, having a severe impact on the health and life of many people globally. One of the crucial step in fighting COVID-19 is the ability to detect the infected patients early enough, and put them under special care. Detecting this disease from radiography and radiology images is perhaps one of the fastest ways to diagnose the patients. Some of the early studies showed specific abnormalities in the chest radiograms of patients infected with COVID-19. Inspired by earlier works, we study the application of deep learning models to detect COVID-19 patients from their chest radiography images. We first prepare a dataset of 5000 Chest X-rays from the publicly available datasets. Images exhibiting COVID-19 disease presence were identified by board-certified radiologist. Transfer learning on a subset of 2000 radiograms was used to train four popular convolutional neural networks, including ResNet18, ResNet50, SqueezeNet, and DenseNet-121, to identify COVID-19 disease in the analyzed chest X-ray images. We evaluated these models on the remaining 3000 images, and most of these networks achieved a sensitivity rate of 98% ( ±  3%), while having a specificity rate of around 90%. Besides sensitivity and specificity rates, we also present the receiver operating characteristic (ROC) curve, precision-recall curve, average prediction, and confusion matrix of each model. We also used a technique to generate heatmaps of lung regions potentially infected by COVID-19 and show that the generated heatmaps contain most of the infected areas annotated by our board certified radiologist. While the achieved performance is very encouraging, further analysis is required on a larger set of COVID-19 images, to have a more reliable estimation of accuracy rates. The dataset, model implementations (in PyTorch), and evaluations, are all made publicly available for research community at https://github.com/shervinmin/DeepCovid.git., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020. Published by Elsevier B.V.)

Background: Inflammation of the arterial wall may lead to aneurysm formation. The presence of aneurysm enhancement on high-resolution vessel wall imaging (HR-VWI) is a marker of wall inflammation and instability. We aim to determine if there is any association between increased contrast enhancement in the aneurysmal wall and its parent artery., Methods: Patients with unruptured intracranial aneurysms (UIAs) prospectively underwent 7T HR-VWI. Regions of interest were selected manually and with a semi-automated protocol based on gradient algorithms of intensity patterns. Mean signal intensities in pre- and post-contrast T1-weighted sequences were adjusted to the enhancement of the pituitary stalk and then subtracted to objectively determine: circumferential aneurysmal wall enhancement (CAWE); parent vessel enhancement (PVE); and reference vessel enhancement (RVE). PVE was assessed over regions located 3- and 5 mm from the aneurysm's neck. RVE was assessed in arteries located in a different vascular territory., Results: Twenty-five UIAs were analyzed. There was a significant moderate correlation between CAWE and 5 mm PVE (Pearson R=0.52, P=0.008), whereas no correlation was found between CAWE and RVE (Pearson R=0.20, P=0.33). A stronger correlation was found between CAWE and 3 mm PVE (Pearson R=0.78, P<0.001). Intra-class correlation analysis demonstrated good reliability between measurements obtained using semi-automated and manual segmentation (ICC coefficient=0.790, 95% CI 0.58 to 0.90)., Conclusion: Parent arteries exhibit higher contrast enhancement in regions closer to the aneurysm's neck, especially in aneurysms≥7 mm. A localized inflammatory/vasculopathic process in the wall of the parent artery may lead to aneurysm formation and growth., Competing Interests: Competing interests: None declared., (© Author(s) (or their employer(s)) 2020. No commercial re-use. See rights and permissions. Published by BMJ.)

Background: We tested whether the level of endothelial dysfunction assessed by digital tonometry, and expressed as reactive hyperemia index (RHI), is related to occurrences of a discrepancy between fractional flow reserve (FFR) and the instantaneous wave free ratio (iFR) (ClinicalTrials.gov identifier: NCT03033810). Methods: We examined patients with coronary stenosis in the range of 40-70%, assessed by both FFR and iFR (system Philips-Volcano) for stable angina. We included consecutive patients with FFR and iFR in one native coronary artery, and who had had no previous intervention. Results: We included 138 patients. Out of those, 24 patients (17.4%) had a negative FFR (with an FFR value >0.8) and positive iFR (with a iFR value ≤0.89) - designated the FFRn/iFRp discrepancy group, and 22 patients (15.9%) had a positive FFR (≤0.8) and negative iFR (>0.89) - designated the FFRp/iFRn discrepancy. RHI was higher in the discrepancy groups compared the group without discrepancy (1.73 ± 0.79 vs. 1.48 ± 0.50, p  = 0.025). However, this finding was not confirmed in multivariant logistic regression analyses. Patients with any type of discrepancy differed from the agreement group by having a higher occurrence of diabetes mellitus [9 patients (21.4%) vs. 36 patients (39.6%), p  = 0.029], active smoking (23 patients or 54.8% vs. 26 patients or 28.6%, p  = 0.003) and lower use of calcium channel blockers (9 patients, 21.4%, vs. 43 patients, 46.7%, p  = 0.004). Conclusion: The presence of endothelial dysfunction can be associated with a discrepancy in FFR/iFR. However, RHI correlated with risk factors of atherosclerosis, not with FFR or iFR.

Introduction: Heart rate slowing agents are frequently prescribed to manage heart transplant (HTx) patients with the assumption that higher heart rate is a risk factor in cardiovascular disease., Patients and Methods: This prospective two-center study investigated early progression of cardiac allograft vasculopathy (CAV) in 116 HTx patients. Examinations by coronary optical coherence tomography and 24-hour ambulatory ECG monitoring were performed both at baseline (1 month after HTx) and during follow-up (12 months after HTx)., Results: During the first post-HTx year, we observed a significant reduction in the mean coronary luminal area from 9.0 ± 2.5 to 8.0 ± 2.4 mm 2 (P < .001), and progression in mean intimal thickness (IT) from 106.5 ± 40.4 to 130.1 ± 53.0 µm (P < .001). No significant relationship was observed between baseline and follow-up mean heart rates and IT progression (R = .02, P = .83; R = -.13, P = .18). We found a mild inverse association between beta-blocker dosage at 12 months and IT progression (R = -.20, P = .035)., Conclusion: Our study did not confirm a direct association between mean heart rate and progression of CAV. The role of beta blockers warrants further investigation, with our results indicating that they may play a protective role in early CAV development., (© 2019 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.)

Cardiac allograft vasculopathy (CAV) accounts for about 30% of all heart-transplant (HTx) patient deaths. For patients at high risk for CAV complications after HTx, therapy must be initiated early to be effective. Therefore, new phenotyping approaches are needed to identify such HTx patients at the earliest possible time. Coronary optical coherence tomography (OCT) images were acquired from 50 HTx patients 1 and 12 months after HTx. Quantitative analysis of coronary wall morphology used LOGISMOS segmentation strategy to simultaneously identify three wall-layer surfaces for the entire pullback length in 3D: luminal, outer intimal, and outer medial surfaces. To quantify changes of coronary wall morphology between 1 and 12 months after HTx, the two pullbacks were mutually co-registered. Validation of layer thickness measurements showed high accuracy of performed layer analyses with layer thickness measures correlating well with manually-defined independent standard (R automated 2  = 0.93, y=1.0x-6.2μm), average intimal+medial thickness errors were 4.98 ± 31.24 µm, comparable with inter-observer variability. Quantitative indices of coronary wall morphology 1 month and 12 months after HTx showed significant local as well as regional changes associated with CAV progression. Some of the newly available fully-3D baseline indices (intimal layer brightness, medial layer brightness, medial thickness, and intimal+medial thickness) were associated with CAV-related progression of intimal thickness showing promise of identifying patients subjected to rapid intimal thickening at 12 months after HTx from OCT-image data obtained just 1 month after HTx. Our approach allows quantification of location-specific alterations of coronary wall morphology over time and is sensitive even to very small changes of wall layer thicknesses that occur in patients following heart transplant., (Copyright © 2018 Elsevier B.V. All rights reserved.)

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., (Copyright © 2018 International Society for the Heart and Lung Transplantation. Published by Elsevier Inc. All rights reserved.)

A fully automated knee magnetic resonance imaging (MRI) segmentation method to study osteoarthritis (OA) was developed using a novel hierarchical set of random forests (RF) classifiers to learn the appearance of cartilage regions and their boundaries. A neighborhood approximation forest is used first to provide contextual feature to the second-level RF classifier that also considers local features and produces location-specific costs for the layered optimal graph image segmentation of multiple objects and surfaces (LOGISMOS) framework. Double-echo steady state MRIs used in this paper originated from the OA Initiative study. Trained on 34 MRIs with varying degrees of OA, the performance of the learning-based method tested on 108 MRIs showed significant reduction in segmentation errors ( ) compared with the conventional gradient-based and single-stage RF-learned costs. The 3-D LOGISMOS was extended to longitudinal-3-D (4-D) to simultaneously segment multiple follow-up visits of the same patient. As such, data from all time-points of the temporal sequence contribute information to a single optimal solution that utilizes both spatial 3-D and temporal contexts. 4-D LOGISMOS validation on 108 MRIs from baseline, and 12 month follow-up scans of 54 patients showed significant reduction in segmentation errors ( ) compared with 3-D. Finally, the potential of 4-D LOGISMOS was further explored on the same 54 patients using five annual follow-up scans demonstrating a significant improvement of measuring cartilage thickness ( ) compared with the sequential 3-D approach.

Features of high-risk coronary artery plaques prone to major adverse cardiac events (MACE) were identified by intravascular ultrasound (IVUS) virtual histology (VH). These plaque features are: thin-cap fibroatheroma (TCFA), plaque burden PB ≥ 70%, or minimal luminal area MLA ≤ 4 mm 2 . Identification of arterial locations likely to later develop such high-risk plaques may help prevent MACE. We report a machine learning method for prediction of future high-risk coronary plaque locations and types in patients under statin therapy. Sixty-one patients with stable angina on statin therapy underwent baseline and one-year follow-up VH-IVUS non-culprit vessel examinations followed by quantitative image analysis. For each segmented and registered VH-IVUS frame pair ( ), location-specific ( mm) vascular features and demographic information at baseline were identified. Seven independent support vector machine classifiers with seven different feature subsets were trained to predict high-risk plaque types one year later. A leave-one-patient-out cross-validation was used to evaluate the prediction power of different feature subsets. The experimental results showed that our machine learning method predicted future TCFA with correctness of 85.9%, 81.7%, and 77.0% (G-mean) for baseline plaque phenotypes of TCFA, thick-cap fibroatheroma, and non-fibroatheroma, respectively. For predicting PB ≥ 70%, correctness was 80.8% for baseline PB ≥ 70% and 85.6% for 50% ≤ PB < 70%. Accuracy of predicted MLA ≤ 4 mm 2 was 81.6% for baseline MLA ≤ 4 mm 2 and 80.2% for 4 mm 2 < MLA ≤ 6 mm 2 . Location-specific prediction of future high-risk coronary artery plaques is feasible through machine learning using focal vascular features and demographic variables. Our approach outperforms previously reported results and shows the importance of local factors on high-risk coronary artery plaque development.

Background: Coronary atherosclerosis progresses faster in patients with diabetes mellitus (DM) and causes higher morbidity and mortality in such patients compared to non-diabetics ones (non-DM). We quantify changes in plaque volume and plaque phenotype during lipid-lowering therapy in DM versus non-DM patients using advanced intracoronary imaging., Methods: We analyzed data from 61 patients with stable angina pectoris included to the PREDICT trial searching for prediction of plaque changes during intensive lipid-lowering therapy (40 mg rosuvastatin daily). Geometrically correct, fully 3-D representation of the vascular wall surfaces and intravascular ultrasound virtual histology (IVUS-VH) defined tissue characterization was obtained via fusion of two-plane angiography and IVUS-VH. Frame-based indices of plaque morphology and virtual histology analyses were computed and averaged in 5 mm long baseline/follow-up registered vessel segments covering the entire length of the two sequential pullbacks (baseline, 1-year). We analyzed 698 5-mm-long segments and calculated the Liverpool active plaque score (LAPS)., Results: Despite reaching similar levels of LDL cholesterol (DM 2.12 ± 0.91 mmol/l, non-DM 1.8 ± 0.66 mmol/l, p = 0.21), DM patients experienced, compared to non-DM ones, higher progression of mean plaque area (0.47 ± 1.15 mm 2 vs. 0.21 ± 0.97, p = 0.001), percent atheroma volume (0.7 ± 2.8% vs. - 1.4 ± 2.5%, p = 0.007), increase of LAPS (0.23 ± 1.66 vs. 0.13 ± 1.79, p = 0.018), and exhibited more locations with TCFA (Thin-Cap Fibro-Atheroma) plaque phenotype in 5 mm vessel segments (20.3% vs. 12.5%, p = 0.01). However, only non-DM patients reached significant decrease of LDL cholesterol. Plaque changes were more pronounced in PIT (pathologic intimal thickening) compared to TCFA with increased plaque area in both phenotypes in DM patients., Conclusion: Based on detailed 3D analysis, we found advanced plaque phenotype and further atherosclerosis progression in DM patients despite the same reached levels of LDLc as in non-DM patients. Trial registration ClinicalTrials.gov identifier: NCT01773512.

This paper introduces a model-based approach for a fully automatic delineation of kidney and cortex tissue from contrast-enhanced abdominal CT scans. The proposed framework, named CorteXpert, consists of two new strategies for kidney tissue delineation: cortex model adaptation and non-uniform graph search. CorteXpert was validated on a clinical data set of 58 CT scans using the cross-validation evaluation strategy. The experimental results indicated the state-of-the-art segmentation accuracies (as dice coefficient): 97.86% ± 2.41% and 97.48% ± 3.18% for kidney and renal cortex delineations, respectively., (Copyright © 2017 Elsevier B.V. All rights reserved.)

Purpose: To identify the spatial distribution of exudative features of choroidal neovascularization in neovascular age-related macular degeneration (nAMD) based on the localization of intraretinal cystoid fluid (IRC), subretinal fluid (SRF), and pigment-epithelial detachment (PED)., Methods: This retrospective cross-sectional study included spectral-domain optical coherence tomography volume scans (6 × 6 mm) of 1341 patients with treatment-naïve nAMD. IRC, SRF, and PED were detected on a per-voxel basis using fully automated segmentation algorithms. Two subsets of 37 volumes each were manually segmented to validate the automated results. The spatial correspondence of components was quantified by computing proportions of IRC-, SRF-, or PED-presenting A-scans simultaneously affected by the respective other pathomorphologic components on a per-patient basis. The median across the population is reported. Odds ratios between pairs of lesions were calculated and tested for significance pixel wise., Results: Automated image segmentation was successful in 1182 optical coherence tomography volumes, yielding more than 61 million A-scans for analysis. Overall, 81% of eyes showed IRC, 95% showed SRF, and 92% showed PED. IRC-presenting A-scans also showed SRF in a median 2.5%, PED in 32.9%. Of the SRF-presenting A-scans, 0.3% demonstrated IRC, 1.4% PED. Of the PED-presenting A-scans, 5.2% contained IRC, 2.0% SRF. Similar patterns were observed in the manually segmented subsets and via pixel-wise odds ratio analysis., Conclusions: Automated analyses of large-scale datasets in a cross-sectional study of 1182 patients with active treatment-naïve nAMD demonstrated low spatial correlation of SRF with IRC and PED in contrast to increased colocalization of IRC and PED. These morphological associations may contribute to our understanding of functional deficits in nAMD.

Purpose: A pilot study showed that prediction of individual Humphrey 24-2 visual field (HVF 24-2) sensitivity thresholds from optical coherence tomography (OCT) image analysis is possible. We evaluate performance of an improved approach as well as 3 other predictive algorithms on a new, fully independent set of glaucoma subjects., Methods: Subjects underwent HVF 24-2 and 9-field OCT (Heidelberg Spectralis) testing. Nerve fiber (NFL), and ganglion cell and inner plexiform (GCL+IPL) layers were cosegmented and partitioned into 52 sectors matching HVF 24-2 test locations. The Wilcoxon rank sum test was applied to test correlation R, root mean square error (RMSE), and limits of agreement (LoA) between actual and predicted thresholds for four prediction models. The training data consisted of the 9-field OCT and HVF 24-2 thresholds of 111 glaucoma patients from our pilot study., Results: We studied 112 subjects (112 eyes) with early, moderate, or advanced primary and secondary open angle glaucoma. Subjects with less than 9 scans (15/112) or insufficient quality segmentations (11/97) were excluded. Retinal ganglion cell axonal complex (RGC-AC) optimized had superior average R = 0.74 (95% confidence interval [CI], 0.67-0.76) and RMSE = 5.42 (95% CI, 5.1-5.7) dB, which was significantly better (P < 0.05/3) than the other three models: Naïve (R = 0.49; 95% CI, 0.44-0.54; RMSE = 7.24 dB; 95% CI, 6.6-7.8 dB), Garway-Heath (R = 0.66; 95% CI, 0.60-0.68; RMSE = 6.07 dB; 95% CI, 5.7-6.5 dB), and Donut (R = 0.67; 95% CI, 0.61-0.69; RMSE = 6.08 dB, 95% CI, 5.8-6.4 dB)., Conclusions: The proposed RGC-AC optimized predictive algorithm based on 9-field OCT image analysis and the RGC-AC concept is superior to previous methods and its performance is close to the reproducibility of HVF 24-2.

RADEval is a tool developed to assess the expected clinical impact of contouring accuracy when comparing manual contouring and semi-automated segmentation. The RADEval tool, designed to process large scale datasets, imported a total of 2,760 segmentation datasets, along with a Simultaneous Truth and Performance Level Estimation (STAPLE) to act as ground truth tumor segmentations. Virtual dose-maps were created within RADEval and two different tumor control probability (TCP) values using a Logistic and a Poisson TCP models were calculated in RADEval using each STAPLE and each dose-map. RADEval also virtually generated a ring of normal tissue. To evaluate clinical impact, two different uncomplicated TCP (UTCP) values were calculated in RADEval by using two TCP-NTCP correlation parameters (δ = 0 and 1). NTCP values showed that semi-automatic segmentation resulted in lower NTCP with an average 1.5 - 1.6 % regardless of STAPLE design. This was true even though each normal tissue was created from each STAPLE (p <; 0.00001). TCP and UTCP presented no statistically significant differences (p ≥ 0.1884). The intra-operator standard deviations (SDs) for TCP, NTCP and UTCP were significantly lower for the semi-automatic segmentation method regardless of STAPLE design (p <; 0.0331). Both intra-and inter-operator SDs of TCP, NTCP and UTCP were significantly lower for semi-automatic segmentation for the STAPLE 1 design (p <;0.0331). RADEval was able to efficiently process 4,920 datasets of two STAPLE designs and successfully assess the expected clinical impact of contouring accuracy.

Bruch's membrane opening-minimum rim width (BMO-MRW) is a recently proposed structural parameter which estimates the remaining nerve fiber bundles in the retina and is superior to other conventional structural parameters for diagnosing glaucoma. Measuring this structural parameter requires identification of BMO locations within spectral domain-optical coherence tomography (SD-OCT) volumes. While most automated approaches for segmentation of the BMO either segment the 2D projection of BMO points or identify BMO points in individual B-scans, in this work, we propose a machine-learning graph-based approach for true 3D segmentation of BMO from glaucomatous SD-OCT volumes. The problem is formulated as an optimization problem for finding a 3D path within the SD-OCT volume. In particular, the SD-OCT volumes are transferred to the radial domain where the closed loop BMO points in the original volume form a path within the radial volume. The estimated location of BMO points in 3D are identified by finding the projected location of BMO points using a graph-theoretic approach and mapping the projected locations onto the Bruch's membrane (BM) surface. Dynamic programming is employed in order to find the 3D BMO locations as the minimum-cost path within the volume. In order to compute the cost function needed for finding the minimum-cost path, a random forest classifier is utilized to learn a BMO model, obtained by extracting intensity features from the volumes in the training set, and computing the required 3D cost function. The proposed method is tested on 44 glaucoma patients and evaluated using manual delineations. Results show that the proposed method successfully identifies the 3D BMO locations and has significantly smaller errors compared to the existing 3D BMO identification approaches., (Published by Elsevier B.V.)

Purpose: To examine the associations of nerve fiber layer (NFL) thickness with other ocular characteristics in older adults., Methods: Participants in the Beaver Dam Eye Study (2008-2010) underwent spectral domain optical coherence tomography (SD-OCT) scans of the optic nerve head, imaging of optic discs, frequency doubling technology (FDT) perimetry, measurement of intraocular pressure (IOP), and an interview concerning their history of glaucoma and use of drops to lower eye pressure. Self-reported histories of glaucoma and the use of drops to lower eye pressure were obtained at follow-up examinations (2014-2016)., Results: NFL thickness measured on OCTs varied by location around the optic nerve. Age was associated with mean NFL thickness. Mean NFL was thinnest in eyes with larger cup/disc (C/D) ratios. Horizontal hemifield defects or other optic nerve-field defects were associated with thinner NFL. NFL in persons who reported taking eye drops for high intraocular pressure was thinner compared to those not taking drops. After accounting for the presence of high intraocular pressure, large C/D ratios or hemifield defects, eyes with thinner NFL in the arcades were more likely (OR = 2.3 for 30 micron thinner NFL, p = 0.04) to have incident glaucoma at examination 5 years later., Conclusion: Retinal NFL thickness was associated with a new history of self-reported glaucoma 5 years later. A trial testing the usefulness of NFL as part of a screening battery for predicting glaucoma in those previously undiagnosed might lead to improved case finding and, ultimately, to diminishing the risk of visual field loss.

A general method is reported for improving the segmentation of abnormal cell nuclei in cervical cytology images. In automation-assisted reading of cervical cytology, one of the essential steps is the segmentation of nuclei. Despite some progress, there is a need to improve the sensitivity, particularly the segmentation of abnormal nuclei. Our method starts with pre-segmenting the nucleus to define the coarse center and size of nucleus, which is used to construct a graph by image unfolding that maps ellipse-like border in the Cartesian coordinate system to lines in the polar coordinate system. The cost function jointly reflects properties of nucleus border and nucleus region. The prior constraints regarding the context of nucleus-cytoplasm position are utilized to modify the local cost functions. The globally optimal path in the constructed graph is then identified by dynamic programming with an iterative approach ensuring an optimal closed contour. Validation of our method was performed on abnormal nuclei from two cervical cell image datasets, Herlev and H&E stained manual liquid-based cytology (HEMLBC). Compared with five state-of-the-art approaches, our graph-search based method shows superior performance., (Copyright © 2017 Elsevier Ltd. All rights reserved.)

Automated and reliable segmentation of subcortical structures from human brain magnetic resonance images is of great importance for volumetric and shape analyses in quantitative neuroimaging studies. However, poor boundary contrast and variable shape of these structures make the automated segmentation a tough task. We propose a 3D graph-based machine learning method, called LOGISMOS-RF, to segment the caudate and the putamen from brain MRI scans in a robust and accurate way. An atlas-based tissue classification and bias-field correction method is applied to the images to generate an initial segmentation for each structure. Then a 3D graph framework is utilized to construct a geometric graph for each initial segmentation. A locally trained random forest classifier is used to assign a cost to each graph node. The max-flow algorithm is applied to solve the segmentation problem. Evaluation was performed on a dataset of T1-weighted MRI's of 62 subjects, with 42 images used for training and 20 images for testing. For comparison, FreeSurfer and FSL approaches were also evaluated using the same dataset. Dice overlap coefficients and surface-to-surfaces distances between the automated segmentation and expert manual segmentations indicate the results of our method are statistically significantly more accurate than the other two methods, for both the caudate (Dice: 0.89 ± 0.03) and the putamen (0.89 ± 0.03).

Introduction and Objectives: Pathologic intimal thickening (PIT) has been considered a benign plaque phenotype. We report plaque phenotypic changes in a baseline/follow-up intravascular ultrasound-based virtual histology study., Methods: A total of 61 patients with stable coronary artery disease were analyzed from the HEAVEN trial (89 patients randomized between routine statin therapy vs atorvastatin 80mg and ezetimibe 10mg) with serial intravascular ultrasound imaging of nonculprit vessels. We compared changes in 693 baseline and follow-up 5-mm long segments in a novel risk score, Liverpool Active Plaque Score (LAPS), plaque parameters, and plaque composition., Results: The PIT showed the highest increase of risk score and, with fibrous plaque, also the LAPS. Necrotic core (NC) abutting to the lumen increased in PIT (22 ± 51.7; P = .0001) and in fibrous plaque (17.9 ± 42.6; P = .004) but decreased in thin cap fibroatheroma (TCFA) (⿿15.14 ± 52.2; P = .001). The PIT was the most likely of all nonthin cap fibroatheroma plaque types to transform into TCFA at follow-up (11% of all TCFA found during follow-up and 35.9% of newly-developed TCFA), but showed (together with fibrous plaque) the lowest stability during lipid-lowering therapy (24.7% of PIT remained PIT and 24.5% of fibrous plaque remained fibrous plaque)., Conclusions: Over the 1-year follow-up, PIT was the most dynamic of the plaque phenotypes and was associated with an increase of risk score and LAPS (together with fibrous plaque), NC percentage (together with fibrous plaque) and NC abutting to the lumen, despite a small reduction of plaque volume during lipid-lowering therapy. The PIT was the main source for new TCFA segments., (Copyright © 2016 Sociedad Española de Cardiología. Published by Elsevier España, S.L.U. All rights reserved.)

We present a fully automated learning-based approach for segmenting knee cartilage in presence of osteoarthritis (OA). The algorithm employs a hierarchical set of two random forest classifiers. The first is a neighborhood approximation forest, the output probability map of which is utilized as a feature set for the second random forest (RF) classifier. The output probabilities of the hierarchical approach are used as cost functions in a Layered Optimal Graph Segmentation of Multiple Objects and Surfaces (LOGISMOS). In this work, we highlight a novel post-processing interaction called just-enough interaction (JEI) which enables quick and accurate generation of a large set of training examples. Disjoint sets of 15 and 13 subjects were used for training and tested on another disjoint set of 53 knee datasets. All images were acquired using double echo steady state (DESS) MRI sequence and are from the osteoarthritis initiative (OAI) database. Segmentation performance using the learning-based cost function showed significant reduction in segmentation errors ( p < 0.05) in comparison with conventional gradient-based cost functions.

Multi-atlas label fusion methods have gained popularity in a variety of segmentation tasks given their attractive performance. Graph-based segmentation methods are widely used given their global optimality guarantee. We propose a novel approach, GOLF, that combines the strengths of these two approaches. GOLF incorporates shape priors to the label-fusion problem and provides a globally optimal solution even for the multi-label scenario, while also leveraging the highly accurate posterior maps from a multi-atlas label fusion approach. We demonstrate GOLF for the joint segmentation of the left and right pairs of caudate, putamen, globus pallidus and nucleus accumbens. Compared to the FreeSurfer and FIRST approaches, GOLF is significantly more accurate on all reported indices for all 8 structures. We also present comparisons to a multi-atlas approach, which reveals further insights on the contributions of the different components of the proposed framework.

Background/aims: The purpose of the study was to create a standardised protocol for choroidal thickness measurements and to determine whether choroidal thickness measurements made on images obtained by spectral domain optical coherence tomography (SD-OCT) and swept source (SS-) OCT from patients with healthy retina are interchangeable when performed manually or with an automatic algorithm., Methods: 36 grid cell measurements for choroidal thickness for each volumetric scan were obtained, which were measured for SD-OCT and SS-OCT with two methods on 18 eyes of healthy volunteers. Manual segmentation by experienced retinal graders from the Vienna Reading Center and automated segmentation on >6300 images of the choroid from both devices were statistically compared., Results: Model-based comparison between SD-OCT/SS-OCT showed a systematic difference in choroidal thickness of 16.26±0.725 μm (p<0.001) for manual segmentation and 21.55±0.725 μm (p<0.001) for automated segmentation. Comparison of automated with manual segmentations revealed small differences in thickness of -0.68±0.513 μm (p=0.1833). The correlation coefficients for SD-OCT and SS-OCT measures within eyes were 0.975 for manual segmentation and 0.955 for automatic segmentation., Conclusion: Choroidal thickness measurements of SD-OCT and SS-OCT indicate that these two devices are interchangeable with a trend of choroidal thickness measurements being slightly thicker on SD-OCT with limited clinical relevance. Use of an automated algorithm to segment choroidal thickness was validated in healthy volunteers., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

The accepted definition of virtual histology intravascular ultrasound (IVUS-VH) thin-cap fibroatheroma (TCFA) is only a modest predictor of plaque rupture (PR). We sought to determine the relation between IVUS-VH findings and culprit lesions with PR using computational analysis. A total of 80 culprit lesions from 80 patients with stable angina (n = 37), unstable angina (n = 20), and myocardial infarction (n = 23) were divided into those with (n = 15) and without PR (n = 65). By use of automated computational analysis, the standard IVUS-VH TCFA criterion and 124 additional criteria were compared. The standard TCFA definition demonstrated modest ability to discriminate lesions with and without PR (sensitivity 87%, specificity 37%, PPV 0.24, and NPV 0.92). Of 124 additional IVUS-VH TCFA definitions, only 2 improved the discriminative ability even modestly. However, a positive correlation was demonstrated between cavity size and necrotic core percentage (r = 0.78, p <0.01) and a negative correlation with percentage of fibrous tissue (r = -0.81, p <0.01). In conclusion, IVUS-VH criteria were only modestly associated with PR, without significant improvement by varying IVUS-VH TCFA features, but IVUS-VH features of ruptured plaques were strongly correlated with cavity size., (Copyright © 2016 Elsevier Inc. All rights reserved.)

Purpose: Longitudinal imaging is becoming more commonplace for studies of disease progression, response to treatment, and healthy maturation. Accurate and reproducible quantification methods are desirable to fully mine the wealth of data in such datasets. However, most current retinal OCT segmentation methods are cross-sectional and fail to leverage the inherent context present in longitudinal sequences of images., Methods: We propose a novel graph-based method for segmentation of multiple three-dimensional (3D) scans over time (termed 3D + time or 4D). The usefulness of this approach in retinal imaging is illustrated in the segmentation of the choroidal surfaces from longitudinal optical coherence tomography (OCT) scans. A total of 3219 synthetic (3070) and patient (149) OCT images were segmented for validation of our approach., Results: The results show that the proposed 4D segmentation method is significantly more reproducible (P < 0.001) than the 3D approach and is significantly more sensitive to temporal changes (P < 0.0001) achieved by the substantial increase of measurement robustness., Conclusions: This is the first automated 4D method for jointly quantifying choroidal thickness in longitudinal OCT studies. Our method is robust to image noise and produces more reproducible choroidal thickness measurements than a sequence of independent 3D segmentations, without sacrificing sensitivity to temporal changes.

Purpose: The purpose of this work was to develop, validate, and compare a highly computer-aided method for the segmentation of hot lesions in head and neck 18F-FDG PET scans., Methods: A semiautomated segmentation method was developed, which transforms the segmentation problem into a graph-based optimization problem. For this purpose, a graph structure around a user-provided approximate lesion centerpoint is constructed and a suitable cost function is derived based on local image statistics. To handle frequently occurring situations that are ambiguous (e.g., lesions adjacent to each other versus lesion with inhomogeneous uptake), several segmentation modes are introduced that adapt the behavior of the base algorithm accordingly. In addition, the authors present approaches for the efficient interactive local and global refinement of initial segmentations that are based on the "just-enough-interaction" principle. For method validation, 60 PET/CT scans from 59 different subjects with 230 head and neck lesions were utilized. All patients had squamous cell carcinoma of the head and neck. A detailed comparison with the current clinically relevant standard manual segmentation approach was performed based on 2760 segmentations produced by three experts., Results: Segmentation accuracy measured by the Dice coefficient of the proposed semiautomated and standard manual segmentation approach was 0.766 and 0.764, respectively. This difference was not statistically significant (p = 0.2145). However, the intra- and interoperator standard deviations were significantly lower for the semiautomated method. In addition, the proposed method was found to be significantly faster and resulted in significantly higher intra- and interoperator segmentation agreement when compared to the manual segmentation approach., Conclusions: Lack of consistency in tumor definition is a critical barrier for radiation treatment targeting as well as for response assessment in clinical trials and in clinical oncology decision-making. The properties of the authors approach make it well suited for applications in image-guided radiation oncology, response assessment, or treatment outcome prediction.

Diabetic retinopathy (DR) has long been recognized as a microvasculopathy, but retinal diabetic neuropathy (RDN), characterized by inner retinal neurodegeneration, also occurs in people with diabetes mellitus (DM). We report that in 45 people with DM and no to minimal DR there was significant, progressive loss of the nerve fiber layer (NFL) (0.25 μm/y) and the ganglion cell (GC)/inner plexiform layer (0.29 μm/y) on optical coherence tomography analysis (OCT) over a 4-y period, independent of glycated hemoglobin, age, and sex. The NFL was significantly thinner (17.3 μm) in the eyes of six donors with DM than in the eyes of six similarly aged control donors (30.4 μm), although retinal capillary density did not differ in the two groups. We confirmed significant, progressive inner retinal thinning in streptozotocin-induced "type 1" and B6.BKS(D)-Lepr(db)/J "type 2" diabetic mouse models on OCT; immunohistochemistry in type 1 mice showed GC loss but no difference in pericyte density or acellular capillaries. The results suggest that RDN may precede the established clinical and morphometric vascular changes caused by DM and represent a paradigm shift in our understanding of ocular diabetic complications.

Purpose: To automatically identify which spectral-domain optical coherence tomography (SD-OCT) scans will provide reliable automated layer segmentations for more accurate layer thickness analyses in population studies., Methods: Six hundred ninety macular SD-OCT image volumes (6.0 × 6.0 × 2.3 mm 3 ) were obtained from one eyes of 690 subjects (74.6 ± 9.7 [mean ± SD] years, 37.8% of males) randomly selected from the population-based Rotterdam Study. The dataset consisted of 420 OCT volumes with successful automated retinal nerve fiber layer (RNFL) segmentations obtained from our previously reported graph-based segmentation method and 270 volumes with failed segmentations. To evaluate the reliability of the layer segmentations, we have developed a new metric, segmentability index SI, which is obtained from a random forest regressor based on 12 features using OCT voxel intensities, edge-based costs, and on-surface costs. The SI was compared with well-known quality indices, quality index (QI), and maximum tissue contrast index (mTCI), using receiver operating characteristic (ROC) analysis., Results: The 95% confidence interval (CI) and the area under the curve (AUC) for the QI are 0.621 to 0.805 with AUC 0.713, for the mTCI 0.673 to 0.838 with AUC 0.756, and for the SI 0.784 to 0.920 with AUC 0.852. The SI AUC is significantly larger than either the QI or mTCI AUC ( P < 0.01)., Conclusions: The segmentability index SI is well suited to identify SD-OCT scans for which successful automated intraretinal layer segmentations can be expected., Translational Relevance: Interpreting the quantification of SD-OCT images requires the underlying segmentation to be reliable, but standard SD-OCT quality metrics do not predict which segmentations are reliable and which are not. The segmentability index SI presented in this study does allow reliable segmentations to be identified, which is important for more accurate layer thickness analyses in research and population studies.

Background and Objective: To use automated segmentation software to analyze spectral-domain optical coherence tomography (SD-OCT) scans and evaluate the effectiveness of aflibercept (Eylea; Regeneron, Tarrytown, NY) in the treatment of patients with exudative age-related macular degeneration (AMD) refractory to other treatments., Patients and Methods: Retrospective chart review of 16 patients refractory to bevacizumab (Avastin; Genentech, South San Francisco, CA)/ranibizumab (Lucentis; Genentech, San Francisco, CA) treatment was conducted. Visual acuity, central foveal thickness (CFT), maximum fluid height, pigment epithelial detachment (PED) volume, sub-retinal fluid (SRF) volume, fluid-free time interval, and adverse effects were evaluated. Automated segmentation analysis was used to quantify improvement., Results: With aflibercept treatment, there was a statistically significant improvement in visual acuity by 1 line (P = .020), in CFT by 74.02 µm (P = .001), and in maximum fluid height by 31.9 µm (P= .011). Total PED and SRF volume also decreased significantly by 1.50 µm(3) × 10(8) µm(3) (P = .013). Anatomic improvement was confirmed by automated segmentation analysis., Conclusion: This study demonstrates utility of automated segmentation software in quantifying anatomic improvement with aflibercept treatment in exudative AMD refractory to other anti-vascular endothelial growth factor treatments., (Copyright 2016, SLACK Incorporated.)

Background and Aim: To study relationships between endothelial dysfunction (ED) and coronary atherosclerosis derived from intravascular ultrasound (IVUS) and virtual histology (VH)., Methods: Endothelial dysfunction was examined by EndoPAT system (Itamar Medical) in 56 patients who underwent IVUS and VH (Volcano corp.). Reactive hyperaemia index (RHI) < 2 was used for definition of ED. IVUS sequences were divided into 5 mm-long non-overlapping and adjacent vessel segments. Plaque phenotype was determined for each frame and 5 mm vessel segment was labeled according to highest frame score (from 0 for "no lesion" to 5 for "thin cap fibroatheroma; TCFA")., Results: IVUS-VH data were collected from 41 patients suitable for three-dimensional analysis. Patients with ED exhibited larger plaque burden than those without ED (0.46 ± 0.08 vs. 0.39 ± 0.07, p = 0.014), smaller lumen area (8.59 ± 2.19 vs. 11.90 ± 3.50, p = 0.016), higher plaque risk score (2.82 ± 1.18 vs. 1.84 ± 0.90, p = 0.012), and higher number of TCFA frames (0.36 ± 0.22 vs. 0.22 ± 0.16, p = 0.038). Relative amounts of fibrous tissue correlated positively with RHI (p = 0.034, r = 0.33). The numbers of fibroatheromas and calcified plaques correlated with RHI inversely (r = -0.34, p = 0.031 and r = -0.32, p = 0.044, respectively)., Conclusions: Endothelial dysfunction correlates with severity and phenotype of coronary lesions and can contribute to non-invasive detection of individuals with higher risk of cardiovascular events.

Cystatin C (CysC), an endogenous inhibitor of cysteine proteases and a sensitive and accurate marker of renal function, is associated with the severity of coronary atherosclerosis assessed by angiography and future cardiovascular events according to previous studies. We aimed to evaluate the association between CysC levels and coronary plaque volume, composition and phenotype assessed by intravascular ultrasound and intravascular ultrasound-derived virtual histology in patients with preserved renal function. Forty-four patients with angiographically documented coronary artery disease and complete intravascular imaging were included in the study. Patients were categorized into tertiles by CysC levels. Subjects in the high CysC tertile had significantly higher mean plaque burden (48.0 % ± 6.9 vs. 42.8 % ± 7.4, P = 0.029), lower mean lumen area (8.1 mm2 ± 1.7 vs. 9.9 mm2 ± 3.1, P = 0.044) and a higher number of 5-mm vessel segments with minimum lumen area < 4 mm2 (17.9 ± 18.9 vs. 6.8 ± 11.7, P = 0.021) compared to patients in the lower tertiles. In addition, CysC levels demonstrated significant positive correlation with the mean plaque burden (r = 0.35, P = 0.021). Neither relative, nor absolute plaque components differed significantly according to CysC tertiles. The Liverpool Active Plaque Score was significantly higher in the high CysC tertile patients (0.91 ± 1.0 vs. 0.18 ± 0.92, P = 0.02). In conclusion, our study demonstrated a significant association of increased CysC levels with more advanced coronary artery disease and higher risk plaque phenotype in patients with preserved renal function.

A novel method is reported for simultaneous registration of location (axial direction) and orientation (circumferential direction) of two intravascular ultrasound (IVUS) pullbacks of the same vessel taken at different times. Monitoring plaque progression or regression (e.g., during lipid treatment) is of high clinical relevance. Our method uses a 3D graph optimization approach, in which the cost function jointly reflects similarity of plaque morphology and plaque/perivascular image appearance. Graph arcs incorporate prior information about temporal correspondence of the two IVUS sequences and limited angular twisting between consecutive IVUS images. Additionally, our approach automatically identifies starting and ending frame pairs in the two IVUS pullbacks. Validation of our method was performed in 29 pairs of IVUS baseline/follow-up pullback sequences consisting of 8 622 IVUS image frames in total. In comparison to manual registration by three experts, the average location and orientation registration errors ranged from 0.72 mm to 0.79 mm and from 7.3(°) to 9.3(°), respectively, all close to the inter-observer variability with no difference being statistically significant (p = NS). Rotation angles determined by our automated approach and expert observers showed high correlation (r(2) of 0.97 to 0.98) and agreed closely (mutual bias between the automated method and expert observers ranged from -1.57(°) to 0.15(°)). Compared with state-of-the-art approaches, the new method offers lower errors in both location and orientation registration. Our method offers highly automated and accurate IVUS pullback registration and can be employed in IVUS-based studies of coronary disease progression, enabling more focal studies of coronary plaque development and transition of vulnerability.

Automated three-dimensional retinal fluid (named symptomatic exudate-associated derangements, SEAD) segmentation in 3D OCT volumes is of high interest in the improved management of neovascular Age Related Macular Degeneration (AMD). SEAD segmentation plays an important role in the treatment of neovascular AMD, but accurate segmentation is challenging because of the large diversity of SEAD size, location, and shape. Here a novel voxel classification based approach using a layer-dependent stratified sampling strategy was developed to address the class imbalance problem in SEAD detection. The method was validated on a set of 30 longitudinal 3D OCT scans from 10 patients who underwent anti-VEGF treatment. Two retinal specialists manually delineated all intraretinal and subretinal fluid. Leave-one-patient-out evaluation resulted in a true positive rate and true negative rate of 96% and 0.16% respectively. This method showed promise for image guided therapy of neovascular AMD treatment.

Purpose: To evaluate the validity of a novel fully automated three-dimensional (3D) method capable of segmenting the choroid from two different optical coherence tomography scanners: swept-source OCT (SS-OCT) and spectral-domain OCT (SD-OCT)., Methods: One hundred eight subjects were imaged using SS-OCT and SD-OCT. A 3D method was used to segment the choroid and quantify the choroidal thickness along each A-scan. The segmented choroidal posterior boundary was evaluated by comparing to manual segmentation. Differences were assessed to test the agreement between segmentation results of the same subject. Choroidal thickness was defined as the Euclidian distance between Bruch's membrane and the choroidal posterior boundary, and reproducibility was analyzed using automatically and manually determined choroidal thicknesses., Results: For SS-OCT, the average choroidal thickness of the entire 6- by 6-mm2 macular region was 219.5 μm (95% confidence interval [CI], 204.9-234.2 μm), and for SD-OCT it was 209.5 μm (95% CI, 197.9-221.0 μm). The agreement between automated and manual segmentations was high: Average relative difference was less than 5 μm, and average absolute difference was less than 15 μm. Reproducibility of choroidal thickness between repeated SS-OCT scans was high (coefficient of variation [CV] of 3.3%, intraclass correlation coefficient [ICC] of 0.98), and differences between SS-OCT and SD-OCT results were small (CV of 11.0%, ICC of 0.73)., Conclusions: We have developed a fully automated 3D method for segmenting the choroid and quantifying choroidal thickness along each A-scan. The method yielded high validity. Our method can be used reliably to study local choroidal changes and may improve the diagnosis and management of patients with ocular diseases in which the choroid is affected.

Optical coherence tomography (OCT) provides not only morphological information but also information about layer-specific optical intensities, which may represent the underlying tissue properties. The purpose of this study is to quantitatively investigate the optical intensity of each retinal layers in central retinal artery occlusion (CRAO). Twenty-nine CRAO cases at acute phase and 33 normal controls were included. Macula-centered 3D OCT images were segmented with a fully-automated Iowa Reference Algorithm into 10 layers. Layer-specific mean intensities were determined and compared between the patient and control groups using multiple regression analysis while adjusting for age and optical intensity of the entire region. The optical intensities were higher in CRAO than in controls in layers spanning from the retinal ganglion cell layer to outer plexiform layer (standardized beta = 0.657 to 0.777, all p < 0.001), possibly due to ischemia. Optical intensities were lower at the photoreceptor, retinal pigment epithelium (RPE), and choroid layers (standardized beta = -0.412 to -0.611, all p < 0.01), possibly due to shadowing effects. Among the intraretinal layers, the inner nuclear layer was identified as the best indicator of CRAO. Our study provides in vivo information of the optical intensity changes in each retinal layer in CRAO patients.