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

Imaging Atherosclerosis.

Advances in atherosclerosis imaging technology and research have provided a range of diagnostic tools to characterize high-risk plaque in vivo; however, these important vascular imaging methods additionally promise great scientific and translational applications beyond this quest. When combined with conventional anatomic- and hemodynamic-based assessments of disease severity, cross-sectional multimodal imaging incorporating molecular probes and other novel noninvasive techniques can add detailed interrogation of plaque composition, activity, and overall disease burden. In the catheterization laboratory, intravascular imaging provides unparalleled access to the world beneath the plaque surface, allowing tissue characterization and measurement of cap thickness with micrometer spatial resolution. Atherosclerosis imaging captures key data that reveal snapshots into underlying biology, which can test our understanding of fundamental research questions and shape our approach toward patient management. Imaging can also be used to quantify response to therapeutic interventions and ultimately help predict cardiovascular risk. Although there are undeniable barriers to clinical translation, many of these hold-ups might soon be surpassed by rapidly evolving innovations to improve image acquisition, coregistration, motion correction, and reduce radiation exposure. This article provides a comprehensive review of current and experimental atherosclerosis imaging methods and their uses in research and potential for translation to the clinic.J.M.T. is supported by a Wellcome Trust research training fellowship (104492/Z/14/Z). M.D is supported by the British Heart Foundation (FS/14/78/31020). N.R.E. is supported by a research training fellowship from the Dunhill Medical Trust (RTF44/0114). A.J.B. is supported by the British Heart Foundation. J.H.F.R. is part-supported by the HEFCE, the NIHR Cambridge Biomedical Research Centre, the British Heart Foundation, and the Wellcome Trust.This is the final version of the article. It first appeared from the American Heart Association via http://dx.doi.org/10.1161/CIRCRESAHA.115.30624

Automated coronary artery calcification scoring in non-gated chest CT: Agreement and reliability

Objective: To determine the agreement and reliability of fully automated coronary artery calcium (CAC) scoring in a lung cancer screening population. Materials and Methods: 1793 low-dose chest CT scans were analyzed (non-contrast-enhanced, non-gated). To establish the reference standard for CAC, first automated calcium scoring was performed using a preliminary version of a method employing coronary calcium atlas and machine learning approach. Thereafter, each scan was inspected by one of four trained raters. When needed, the raters corrected initially automaticity-identified results. In addition, an independent observer subsequently inspected manually corrected results and discarded scans with gross segmentation errors. Subsequently, fully automatic coronary calcium scoring was performed. Agatston score, CAC volume and number of calcifications were computed. Agreement was determined by calculating proportion of agreement and examining Bland-Altman plots. Reliability was determined by calculating linearly weighted kappa (κ) for Agatston strata and intraclass correlation coefficient (ICC) for continuous values. Results: 44 (2.5%) scans were excluded due to metal artifacts or gross segmentation errors. In the remaining 1749 scans, median Agatston score was 39.6 (P25-P75:0-345.9), median volume score was 60.4 mm3 (P25-P75:0-361.4) and median number of calcifications was 2 (P25-P75:0-4) for the automated scores. The k demonstrated very good reliability (0.85) for Agatston risk categories between the automated and reference scores. The Bland-Altman plots showed underestimation of calcium score values by automated quantification. Median difference was 2.5 (p25-p75:0.0-53.2) for Agatston score, 7.6 (p25-p75:0.0-94.4) for CAC volume and 1 (p25-p75:0-5) for number of calcifications. The ICC was very good for Agatston score (0.90), very good for calcium volume (0.88) and good for number of calcifications (0.64). Discussion: Fully automated coron

Different lower extremity arterial calcification patterns in patients with chronic limb-threatening ischemia compared with asymptomatic controls

Objectives: The most severe type of peripheral arterial disease (PAD) is critical limb-threatening ischemia (CLI). In CLI, calcification of the vessel wall plays an important role in symptoms, amputation rate, and mortality. However, calcified arteries are also found in asymptomatic persons (non-PAD patients). We investigated whether the calcification pattern in CLI patients and non- PAD patients are different and could possibly explain the symptoms in CLI patients. Materials and Methods: 130 CLI and 204 non-PAD patients underwent a CT of the lower extremities. This resulted in 118 CLI patients (mean age 72 ± 12, 70.3% male) that were age-matched with 118 non-PAD patients (mean age 71 ± 11, 51.7% male). The characteristics severity, annularity, thickness, and continuity were assessed in the femoral and crural arteries and analyzed by binary multiple logistic regression. Results: Nearly all CLI patients have calcifications and these are equally frequent in the femoropopliteal (98.3%) and crural arteries (97.5%), while the non-PAD patients had in just 67% any calcifications with more calcifications in the femoropopliteal (70.3%) than in the crural arteries (55.9%, p < 0.005). The crural arteries of CLI patients had significantly more complete annular calcifications (OR 2.92, p = 0.001), while in non-PAD patients dot-like calcifications dominated. In CLI patients, the femoropopliteal arteries had more severe, irregular/patchy, and thick calcifications (OR 2.40, 3.27, 1.81, p ≤ 0.05, respectively) while in non-PAD patients, thin continuous calcifications prevailed. Conclusions: Compared with non-PAD patients, arteries of the lower extremities of CLI patients are more frequently and extensively calcified. Annular calcifications were found in the crural arteries of CLI patients while dot-like calcifications were mostly present in non-PAD patients. These different patterns of calcifications in CLI point at different etiology and can have prognostic and eventually therapeutic consequences

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