DCNN-Based Automatic Segmentation and Quantification of Aortic Thrombus Volume: Influence of the Training Approach

Deep Learning in Cardiology

“…Method Application/Notes a CT Lessman 2016 [195] CNN detect coronary calcium using three independently trained CNNs Shadmi 2018 [196] DenseNet compared DenseNet and u-net for detecting coronary calcium Cano 2018 [197] CNN 3D regression CNN for calculation of the Agatston score Wolterink 2016 [198] CNN detect coronary calcium using three CNNs for localization and two CNNs for detection Santini 2017 [199] CNN coronary calcium detection using a seven layer CNN on image patches Lopez 2017 [200] CNN thrombus volume characterization using a 2D CNN and postprocessing Hong 2016 [201] DBN detection, segmentation, classification of abdominal aortic aneurysm using DBN and image patches Liu 2017 [202] CNN left atrium segmentation using a twelve layer CNN and active shape model (STA13) de Vos 2016 [203] CNN 3D localization of anatomical structures using three CNNs, one for each orthogonal plane Moradi 2016 [204] CNN detection of position for a given CT slice using a pretrained VGGnet, handcrafted features and SVM Zheng 2015 [205] Multiple carotid artery bifurcation detection using multi-layer perceptrons and probabilistic boosting-tree Montoya 2018 [206] ResNet 3D reconstruction of cerebral angiogram using a 30 layer ResNet Zreik 2018 [207] CNN, AE identify coronary artery stenosis using CNN for LV segmentation and an AE, SVM for classification Commandeur 2018 [208] CNN quantification of epicardial and thoracic adipose tissue from non-contrast CT Gulsun 2016 [209] CNN extract coronary centerline using optimal path from computed flow field and a CNN for refinement CNN carotid intima media thickness video interpretation using two CNNs with two layers on Ultrasound Tom 2017 [226] GAN IVUS image generation using two GANs (IV11) Wang 2017 [227] CNN breast arterial calcification using a ten layer CNN on mammograms Liu 2017 [228] CNN CAC detection using CNNs on 1768 X-Rays Pavoni 2017 [229] CNN denoising of percutaneous transluminal coronary angioplasty images using four layer CNN Nirschl 2018 [230] CNN trained a patch-based six layer CNN for identifying heart failure in endomyocardial biopsy images Betancur 2018 [231] CNN trained a three layer CNN for obstructive CAD prediction from myocardial perfusion imaging a Results from these imaging modalities are not reported in this review because they were highly variable in terms of the research question they were trying to solve and highly inconsistent in respect with the use of metrics. Additionally all papers use private databases besides…”

“…CT has been used for segmentation of various cardiac areas. Lopez et al [200] trained a 2D CNN for aortic thrombus volume assessment from pre-operatively and post-operatively segmentations using rotating and mirroring augmentation.…”

Breast pectoral muscle segmentation in mammograms using a modified holistically-nested edge detection network

“…Following the studies of (Xie and Tu, 2015), (López-Linares et al, 2017) and (Roth et al, 2018), the network weights were initialised from the weights of the original HED network trained on natural images, which helped in dealing with over-fitting and accelerated convergence. We used the stochastic gradient descent optimizer, with a learning rate of 0.01, step-wise learning rate decay policy and momentum of 0.9.…”

Abdominal Aortic Aneurysm Segmentation with a Small Number of Training Subjects

“…For example, a deep belief network was utilized to detect, segment, and classify the AAA [1], however, without quantified segmentation results provided. A deep convolutional neural network (DCNN)based segmentation and quantification of abdominal aortic thrombus [2] was implemented in totally 38 pre-and post-operative subjects to assess the EVAR treatments [2]. A multi-stage network including a detection network (holistically-nested edge detection network) and a segmentation network (fully convolutional network) was proposed to segment the thrombi from 13 post-operative CTA volumes [3].…”