A hybrid learning approach for semantic labeling of cardiac CT slices and recognition of body position

Moradi, Mehdi; Gur, Yaniv; Wang, Hongzhi; Prasanna, Prasanth; Syeda-Mahmood, Tanveer

doi:10.1109/isbi.2016.7493533

Cited by 16 publications

(28 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…For example, approaches using deep learning algorithm in combination with deformable models and level set were proposed for fully automated LVM segmentation in cardiac MRI. A hybrid learning approach integrating a deep convolutional neural network (CNN), support vector machine classifier, and a linear model was developed for semantic labeling for chest CT . For LVM segmentation in cardiac ultrasound, deep learning was combined with methods, for example, derivative‐based search methods, transfer learning, search algorithms .…”

Section: Introductionmentioning

confidence: 99%

Automated left ventricular myocardium segmentation using 3D deeply supervised attention U‐net for coronary computed tomography angiography; CT myocardium segmentation

Guo

Lei

et al. 2020

Medical Physics

View full text Add to dashboard Cite

Purpose Segmentation of left ventricular myocardium (LVM) in coronary computed tomography angiography (CCTA) is important for diagnosis of cardiovascular diseases. Due to poor image contrast and large variation in intensity and shapes, LVM segmentation for CCTA is a challenging task. The purpose of this work is to develop a region‐based deep learning method to automatically detect and segment the LVM solely based on CCTA images. Methods We developed a 3D deeply supervised U‐Net, which incorporates attention gates (AGs) to focus on the myocardial boundary structures, to segment LVM contours from CCTA. The deep attention U‐Net (DAU‐Net) was trained on the patients’ CCTA images, with a manual contour‐derived binary mask used as the learning‐based target. The network was supervised by a hybrid loss function, which combined logistic loss and Dice loss to simultaneously measure the similarities and discrepancies between the prediction and training datasets. To evaluate the accuracy of the segmentation, we retrospectively investigated 100 patients with suspected or confirmed coronary artery disease (CAD). The LVM volume was segmented by the proposed method and compared with physician‐approved clinical contours. Quantitative metrics used were Dice similarity coefficient (DSC), Hausdorff distance (HD), mean surface distance (MSD), residual mean square distance (RMSD), the center of mass distance (CMD), and volume difference (VOD). Results The proposed method created contours with very good agreement to the ground truth contours. Our proposed segmentation approach is benchmarked primarily using fivefold cross validation. Model prediction correlated and agreed well with manual contour. The mean DSC of the contours delineated by our method was 91.6% among all patients. The resultant HD was 6.840 ± 4.410 mm. The proposed method also resulted in a small CMD (1.058 ± 1.245 mm) and VOD (1.640 ± 1.777 cc). Among all patients, the MSD and RMSD were 0.433 ± 0.209 mm and 0.724 ± 0.375 mm, respectively, between ground truth and LVM volume resulting from the proposed method. Conclusions We developed a novel deep learning‐based approach for the automated segmentation of the LVM on CCTA images. We demonstrated the high accuracy of the proposed learning‐based segmentation method through comparison with ground truth contour of 100 clinical patient cases using six quantitative metrics. These results show the potential of using automated LVM segmentation for computer‐aided delineation of CADs in the clinical setting.

show abstract

Section: Introductionmentioning

confidence: 99%

Automated left ventricular myocardium segmentation using 3D deeply supervised attention U‐net for coronary computed tomography angiography; CT myocardium segmentation

Guo

Lei

et al. 2020

Medical Physics

View full text Add to dashboard Cite

show abstract

“…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…”

Section: Referencementioning

confidence: 99%

“…While a single CNN predicted the presence of a specific ROI in the given plane, the combination of their results provided a 3D bounding box around it. In their article Moradi et al [204] address the problem of detection of vertical position for a given cardiac CT slice. They divide the body area depicted in chest CT into nine semantic categories each representing an area most relevant to the study of a disease.…”

Section: Referencementioning

confidence: 99%

Deep Learning in Cardiology

Bizopoulos

Koutsouris

2019

IEEE Rev. Biomed. Eng.

142

View full text Add to dashboard Cite

The medical field is creating large amount of data that physicians are unable to decipher and use efficiently. Moreover, rule-based expert systems are inefficient in solving complicated medical tasks or for creating insights using big data. Deep learning has emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction and intervention. Deep learning is a representation learning method that consists of layers that transform the data non-linearly, thus, revealing hierarchical relationships and structures. In this review we survey deep learning application papers that use structured data, signal and imaging modalities from cardiology. We discuss the advantages and limitations of applying deep learning in cardiology that also apply in medicine in general, while proposing certain directions as the most viable for clinical use.

show abstract

“…The availability of big data repositories, though dissimilar, has made transfer learning a suitable choice for pretraining and adapting CNNs for the medical imaging domain (75,81). Additionally, research has shown that transfer learning can be exploited in data scarce scenarios even when the knowledge transferred is derived from unrelated domains such as natural images (73,82).…”

Section: Issues and Potential Solutionsmentioning

confidence: 99%

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

Loh¹,

Then²

2017

mHealth

View full text Add to dashboard Cite

Cardiovascular diseases are one of the top causes of deaths worldwide. In developing nations and rural areas, difficulties with diagnosis and treatment are made worse due to the deficiency of healthcare facilities. A viable solution to this issue is telemedicine, which involves delivering health care and sharing medical knowledge at a distance. Additionally, mHealth, the utilization of mobile devices for medical care, has also proven to be a feasible choice. The integration of telemedicine, mHealth and computer-aided diagnosis systems with the fields of machine and deep learning has enabled the creation of effective services that are adaptable to a multitude of scenarios. The objective of this review is to provide an overview of heart disease diagnosis and management, especially within the context of rural healthcare, as well as discuss the benefits, issues and solutions of implementing deep learning algorithms to improve the efficacy of relevant medical applications.

show abstract

A hybrid learning approach for semantic labeling of cardiac CT slices and recognition of body position

Cited by 16 publications

References 8 publications

Automated left ventricular myocardium segmentation using 3D deeply supervised attention U‐net for coronary computed tomography angiography; CT myocardium segmentation

Automated left ventricular myocardium segmentation using 3D deeply supervised attention U‐net for coronary computed tomography angiography; CT myocardium segmentation

Deep Learning in Cardiology

Deep learning for cardiac computer-aided diagnosis: benefits, issues & solutions

Contact Info

Product

Resources

About