Deep Learning from Dual-Energy Information for Whole-Heart Segmentation in Dual-Energy and Single-Energy Non-Contrast-Enhanced Cardiac CT

Deep learning-based whole-heart segmentation in coronary CT angiography (CCTA) allows the extraction of quantitative imaging measures for cardiovascular risk prediction. Automatic extraction of these measures in patients undergoing only non-contrast-enhanced CT (NCCT) scanning would be valuable. In this work, we leverage information provided by a dual-layer detector CT scanner to obtain a reference standard in virtual non-contrast (VNC) CT images mimicking NCCT images, and train a 3D convolutional neural network (CNN) for the segmentation of VNC as well as NCCT images. Contrast-enhanced acquisitions on a dual-layer detector CT scanner were reconstructed into a CCTA and a perfectly aligned VNC image. In each CCTA image, manual reference segmentations of the left ventricular (LV) myocardium, LV cavity, right ventricle, left atrium, right atrium, ascending aorta, and pulmonary artery trunk were obtained and propagated to the corresponding VNC image. These VNC images and reference segmentations were used to train 3D CNNs for automatic segmentation in either VNC images or NCCT images. Automatic segmentations in VNC images showed good agreement with reference segmentations, with an average Dice similarity coefficient of 0.897 \pm 0.034 and an average symmetric surface distance of 1.42 \pm 0.45 mm. Volume differences [95% confidence interval] between automatic NCCT and reference CCTA segmentations were -19 [-67; 30] mL for LV myocardium, -25 [-78; 29] mL for LV cavity, -29 [-73; 14] mL for right ventricle, -20 [-62; 21] mL for left atrium, and -19 [-73; 34] mL for right atrium, respectively. In 214 (74%) NCCT images from an independent multi-vendor multi-center set, two observers agreed that the automatic segmentation was mostly accurate or better. This method might enable quantification of additional cardiac measures from NCCT images for improved cardiovascular risk prediction

Влияние циркуляции вод на загрязнение прибрежных акваторий Керченской бухты соединениями тяжелых металлов и нефтепродуктов

Исследование связи атмосферных переносов над Керченским проливом с загрязнением акватории Керченского морского торгового порта и других прибрежных участков акватории Керченской бухты соединениями тяжелых металлов и нефтепродуктов в 1993 – 2006 гг. позволили установить ключевую роль черноморского типа течений в проливе и локальной циркуляции вод в Керченской бухте в загрязнении исследуемых акваторий.Дослідження зв'язку атмосферних перенесень над Керченською протокою із забрудненням акваторії Керченського морського торгового порту і інших прибережних ділянок акваторії Керченської бухти сполуками важких металів і нафтопродуктів в 1993 – 2006 рр. дозволили встановити ключову роль чорноморського типу течій в протоці і локальної циркуляції вод в Керченській бухті в забрудненні досліджуваних акваторій.Research of connection of atmospheric transport over the Kerch Strait and water area pollution of Kerch Trading Sea Port and other coastal areas of the of the Kerch bay by heavy metals and petroleum products in 1993 – 2006, have established the key role the Black Sea type currents in the strait and the local water circulation in the Bay of Kerch in the pollution study waters

Deep Learning-Based Regression and Classification for Automatic Landmark Localization in Medical Images

In this study, we propose a fast and accurate method to automatically localize anatomical landmarks in medical images. We employ a global-to-local localization approach using fully convolutional neural networks (FCNNs). First, a global FCNN localizes multiple landmarks through the analysis of image patches, performing regression and classification simultaneously. In regression, displacement vectors pointing from the center of image patches towards landmark locations are determined. In classification, presence of landmarks of interest in the patch is established. Global landmark locations are obtained by averaging the predicted displacement vectors, where the contribution of each displacement vector is weighted by the posterior classification probability of the patch that it is pointing from. Subsequently, for each landmark localized with global localization, local analysis is performed. Specialized FCNNs refine the global landmark locations by analyzing local sub-images in a similar manner, i.e. by performing regression and classification simultaneously and combining the results. Evaluation was performed through localization of 8 anatomical landmarks in CCTA scans, 2 landmarks in olfactory MR scans, and 19 landmarks in cephalometric X-rays. We demonstrate that the method performs similarly to a second observer and is able to localize landmarks in a diverse set of medical images, differing in image modality, image dimensionality, and anatomical coverage.Comment: 12 pages, accepted at IEEE transactions in Medical Imagin

Automatic coronary artery calcium scoring on radiotherapy planning CT Scans of breast cancer patients: Reproducibility and association with traditional cardiovascular risk factors

Objectives Coronary artery calcium (CAC) is a strong and independent predictor of cardiovascular disease (CVD) risk. This study assesses reproducibility of automatic CAC scoring on radiotherapy planning computed tomography (CT) scans of breast cancer patients, and examines its association with traditional cardiovascular risk factors. Methods This study included 561 breast cancer patients undergoing radiotherapy between 2013 and 2015. CAC was automatically scored with an algorithm using supervised pattern recognition, expressed as Agatston scores and categorized into five categories (0, 1-10, 11-100, 101-400, >400). Reproducibility between automatic and manual expert scoring was assessed in 79 patients with automatically determined CAC above zero and 84 randomly selected patients without automatically determined CAC. Interscan reproducibility of automatic scoring was assessed in 294 patients having received two scans (82% on the same day). Association between CAC and CVD risk factors was assessed in 36 patients with CAC scores >100, 72 randomly selected patients with scores 1-100, and 72 randomly selected patients without CAC. Reliability was assessed with linearly weighted kappa and agreement with proportional agreement. Results 134 out of 561 (24%) patients had a CAC score above zero. Reliability of CVD risk categorization between automatic and manual scoring was 0.80 (95% Confidence Interval (CI): 0.74-0.87), and slightly higher for scans with breath-hold. Agreement was 0.79 (95% CI: 0.72-0.85). Interscan reliability was 0.61 (95% CI: 0.50-0.72) with an agreement of 0.84 (95% CI: 0.80-0.89). Ten out of 36 (27.8%) patients with CAC scores above 100 did not have other cardiovascular risk factors. Conclusions Automatic CAC scoring on radiotherapy planning CT scans is a reliable method to assess CVD risk based on Agatston scores. One in four breast cancer patients planned for radiotherapy have elevated CAC score. One in three patients with high CAC scores don't have other CVD risk factors and wouldn't have been identified as high risk

PET Molecular Targets and Near-Infrared Fluorescence Imaging of Atherosclerosis

PURPOSE OF REVIEW: With this review, we aim to summarize the role of positron emission tomography (PET) and near-infrared fluorescence imaging (NIRF) in the detection of atherosclerosis. RECENT FINDINGS: (18)F-FDG is an established measure of increased macrophage activity. However, due to its low specificity, new radiotracers have emerged for more specific detection of vascular inflammation and other high-risk plaque features such as microcalcification and neovascularization. Novel NIRF probes are engineered to sense endothelial damage as an early sign of plaque erosion as well as oxidized low-density lipoprotein (oxLDL) as a prime target for atherosclerosis. Integrated NIRF/OCT (optical coherence tomography) catheters enable to detect stent-associated microthrombi. Novel radiotracers can improve specificity of PET for imaging atherosclerosis. Advanced NIRF probes show promise for future application in human. Intravascular NIRF might play a prominent role in the detection of stent-induced vascular injury