Deep Learning for Cardiac Image Segmentation: A Review

Chen, Chen; Chen, Qin; Qiu, Huaqi; Tarroni, Giacomo; Duan, Jinming; Bai, Wenjia; Rueckert, Daniel

doi:10.3389/fcvm.2020.00025

Cited by 607 publications

(435 citation statements)

References 214 publications

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“…Recently, advances in machine learning and artificial intelligence, especially those related to deep learning architectures [37], have revolutionised image processing tasks [38][39][40][41][42][43]. Several deep learning architectures [44][45][46] have obtained outstanding results in difficult tasks such as those of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) [47].…”

Section: Introductionmentioning

confidence: 99%

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

et al. 2020

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The quantitative study of cell morphology is of great importance as the structure and condition of cells and their structures can be related to conditions of health or disease. The first step towards that, is the accurate segmentation of cell structures. In this work, we compare five approaches, one traditional and four deep-learning, for the semantic segmentation of the nuclear envelope of cervical cancer cells commonly known as HeLa cells. Images of a HeLa cancer cell were semantically segmented with one traditional image-processing algorithm and four three deep learning architectures: VGG16, ResNet18, Inception-ResNet-v2, and U-Net. Three hundred slices, each 2000 × 2000 pixels, of a HeLa Cell were acquired with Serial Block Face Scanning Electron Microscopy. The first three deep learning architectures were pre-trained with ImageNet and then fine-tuned with transfer learning. The U-Net architecture was trained from scratch with 36, 000 training images and labels of size 128 × 128. The image-processing algorithm followed a pipeline of several traditional steps like edge detection, dilation and morphological operators. The algorithms were compared by measuring pixel-based segmentation accuracy and Jaccard index against a labelled ground truth. The results indicated a superior performance of the traditional algorithm (Accuracy = 99%, Jaccard = 93%) over the deep learning architectures:

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Section: Introductionmentioning

confidence: 99%

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

et al. 2020

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“…In this study, GDL U-Net segmented the right ventricle with an average DSC of 0.912 (95% HD: 4.242 mm), which is relatively better than Tran's segmentation results.In addition, most of the current researches on the automatic segmentation of the heart are single-organ segmentation, and only a few researches on the automatic segmentation of the heart and two ventricles. We are the first to use a neural network combining different loss functions to automatically segment the ten substructures of the heart.The retrospective analysis of Chen et al (10) verified the previously appeared cardiac biventricular segmentation algorithm on the same data set.…”

Section: Discussionmentioning

confidence: 71%

“…However, they usually require a large amount of feature engineering knowledge or prior knowledge to obtain satisfactory accuracy, and their segmentation results for adjacent organs with inconspicuous gray gradients are not good enough, and the automatic segmentation runs longer (10)(11).…”

mentioning

confidence: 99%

Compare the Segmentation Accuracy of Heart Substructure on Contrast Enhanced CT by Deep Convolutional Neural Network with Different Loss Functions

Huang¹,

Han²,

Wang³

et al. 2020

Preprint

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Background: We aimed to compare the segmentation accuracy of heart substructure on contrast enhanced CT by deep neural network combined with different loss functions.Methods: We collected 35 thoracic tumor patients admitted to the Department of Radiation Oncology of Yunnan Cancer Hospital. Organ-at-risks (OARs) were defined as 10 organs of cardiac substructures (pericardium, heart, left atrium, left ventricle, right atrium, right ventricle, left main stem, left anterior descending Branch, left circumflex branch, right coronary artery), and use the OARs manually outlined by radiation oncologists on enhanced localization CT as the gold standard. The automatic segmentation results of GDL U-Net, WCEGDL U-Net, ELL U-Net, and GDL V-Net are compared with the gold standard. DSC, JC, HD, VD are used as quantitative evaluation indicators. Results: The segmentation DSC of the pericardium, heart, atrium, and ventricle of the DCNN with different loss functions all reached above 0.87. WCEGDL U-Net segmented the pericardium with DSC of 0.961 and 95% HD of 3.449mm; The segmentation DSC of the heart by ELL U-Net reached 0.965, and the 95% HD was 3.477mm; GDL U-Net segmentation of left atrium and right ventricle is better, DSC is 0.896 (95% HD: 3.429mm), 0.912 (95% HD: 4.242mm);GDL V-Net has better segmentation performance for right atrium and left ventricle, with DSC of 0.881 (95% HD: 3.904mm) and 0.940 (95% HD: 2.821mm). Conclusions: The DCNN proposed in this study have achieved better segmentation effects on the pericardium, heart and four chambers in cardiac substructure segmentation.

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“…Image segmentation is of great interest in medical imaging, e.g. in imaging of tumors (1,2), retina (3), lung (4), and the heart (5). In the latter, segmentation is applied to partition acquired images into functionally meaningful regions.…”

Section: Introductionmentioning

confidence: 99%

Sensitivity Analysis for Interpretation of Machine Learning Based Segmentation Models in Cardiac MRI

Ankenbrand

Shainberg

Hock

et al. 2020

Preprint

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Background Image segmentation is a common task in medical imaging e.g. for volumetry analysis in cardiac MRI. Artificial neural networks are used to automate this task with performance similar to manual operators. However, this performance is only achieved in the narrow tasks networks are trained on. Performance drops dramatically when data characteristics differ from the training set properties. Moreover, neural networks are commonly considered black boxes, because it is hard to understand how they make decisions and why they fail. Therefore, it is also hard to predict whether they will generalize and work well with new data. Results We show that sensitivity analysis is a suitable approach to answer practical questions regarding use and functionality of segmentation models. We also provide an open source Python library (misas), that facilitates the use of this method with arbitrary data and models. By enabling a better understanding of neural networks through sensitivity analysis it also assists in decision making. We demonstrate this in two case studies on cardiac magnetic resonance imaging.Conclusions Sensitivity analysis is a useful tool for deep learning developers as well as users such as clinicians. It extends their toolbox with a new tool that makes segmentation models more interpretable. Although demonstrated only on cardiac magnetic resonance images this approach and software are much more broadly applicable.

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Deep Learning for Cardiac Image Segmentation: A Review

Cited by 607 publications

References 214 publications

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

Semantic segmentation of HeLa cells: An objective comparison between one traditional algorithm and four deep-learning architectures

Compare the Segmentation Accuracy of Heart Substructure on Contrast Enhanced CT by Deep Convolutional Neural Network with Different Loss Functions

Sensitivity Analysis for Interpretation of Machine Learning Based Segmentation Models in Cardiac MRI

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