Automatic Segmentation of LV and RV in Cardiac MRI

Jang, Yeonggul; Hong, Yoonmi; Ha, Seongmin; Kim, Sekeun; Chang, Hyuk Jae

doi:10.1007/978-3-319-75541-0_17

Cited by 49 publications

(32 citation statements)

References 11 publications

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“…It can be observed that the proposed method is obviously superior to the other methods for RV and MYO segmentation. For the LV segmentation, the method by Khened achieves the best Dice score and Jang achieves the best HD score, and our result is very close to the optimal value in each metric. Overall, the above experimental results show that the proposed method achieves noticeable improvements against the single model based state‐of‐the‐art methods of the ACDC Challenge.…”

Section: Resultssupporting

confidence: 63%

An iterative multi‐path fully convolutional neural network for automatic cardiac segmentation in cine MR images

Wang³

et al. 2019

Medical Physics

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Purpose Segmentation of the left ventricle (LV), right ventricle (RV) cavities and the myocardium (MYO) from cine cardiac magnetic resonance (MR) images is an important step for diagnosis and monitoring cardiac diseases. Spatial context information may be highly beneficial for segmentation performance improvement. To this end, this paper proposes an iterative multi‐path fully convolutional network (IMFCN) to effectively leverage spatial context for automatic cardiac segmentation in cine MR images. Methods To effectively leverage spatial context information, the proposed IMFCN explicitly models the interslice spatial correlations using a multi‐path late fusion strategy. First, the contextual inputs including both the adjacent slices and the already predicted mask of the above adjacent slice are processed by independent feature‐extraction paths. Then, an atrous spatial pyramid pooling (ASPP) module is employed at the feature fusion process to combine the extracted high‐level contextual features in a more effective way. Finally, deep supervision (DS) and batch‐wise class re‐weighting mechanism are utilized to enhance the training of the proposed network. Results The proposed IMFCN was evaluated and analyzed on the MICCAI 2017 automatic cardiac diagnosis challenge (ACDC) dataset. On the held‐out training dataset reserved for testing, our method effectively improved its counterparts that without spatial context and that with spatial context but using an early fusion strategy. On the 50 subjects test dataset, our method achieved Dice similarity coefficient of 0.935, 0.920, and 0.905, and Hausdorff distance of 7.66, 12.10, and 8.80 mm for LV, RV, and MYO, respectively, which are comparable or even better than the state‐of‐the‐art methods of ACDC Challenge. In addition, to explore the applicability to other datasets, the proposed IMFCN was retrained on the Sunnybrook dataset for LV segmentation and also produced comparable performance to the state‐of‐the‐art methods. Conclusions We have presented an automatic end‐to‐end fully convolutional architecture for accurate cardiac segmentation. The proposed method provides an effective way to leverage spatial context in a two‐dimensional manner and results in precise and consistent segmentation results.

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Section: Resultssupporting

confidence: 63%

An iterative multi‐path fully convolutional neural network for automatic cardiac segmentation in cine MR images

Wang³

et al. 2019

Medical Physics

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“…Four papers used a modified version of the U-Net. Jang et al [45] implemented a "M-Net" [55] architecture whose main difference with U-Net resides in the feature maps of the decoding layers which are concatenated with those of the previous layer. The corresponding network was trained with a weighted cross-entropy loss.…”

Section: A Architectures For Cardiac Multi-structure Segmentationmentioning

confidence: 99%

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Bernard

Lalande

Zotti

et al. 2018

IEEE Trans. Med. Imaging

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1,422

834

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Delineation of the left ventricular cavity, myocardium, and right ventricle from cardiac magnetic resonance images (multi-slice 2-D cine MRI) is a common clinical task to establish diagnosis. The automation of the corresponding tasks has thus been the subject of intense research over the past decades. In this paper, we introduce the "Automatic Cardiac Diagnosis Challenge" dataset (ACDC), the largest publicly available and fully annotated dataset for the purpose of cardiac MRI (CMR) assessment. The dataset contains data from 150 multi-equipments CMRI recordings with reference measurements and classification from two medical experts. The overarching objective of this paper is to measure how far state-of-the-art deep learning methods can go at assessing CMRI, i.e., segmenting the myocardium and the two ventricles as well as classifying pathologies. In the wake of the 2017 MICCAI-ACDC challenge, we report results from deep learning methods provided by nine research groups for the segmentation task and four groups for the classification task. Results show that the best methods faithfully reproduce the expert analysis, leading to a mean value of 0.97 correlation score for the automatic extraction of clinical indices and an accuracy of 0.96 for automatic diagnosis. These results clearly open the door to highly accurate and fully automatic analysis of cardiac CMRI. We also identify scenarios for which deep learning methods are still failing. Both the dataset and detailed results are publicly available online, while the platform will remain open for new submissions.

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“…In addition, there are several variants of the crossentropy or soft-Dice loss such as the weighted cross-entropy loss (Jang et al, 2017;Baumgartner et al, 2017) and weighted soft-Dice loss (Yang et al, 2017c;Khened et al, 2019) that are used to address potential class imbalance problem in medical image segmentation tasks where the loss term is weighted to account for rare classes or small objects.…”

Section: Common Loss Functionsmentioning

confidence: 99%

“…LGE MR imaging enables the Table 2. Segmentation accuracy of state-of-the-art segmentation methods verified on the cardiac bi-ventricular segmentation challenge (ACDC) dataset All the methods were evaluated on the same test set (50 subjects 2D GridNet-MD with registered shape prior 0.938 0.894 0.910 Khened et al (2019) 2D Dense U-net with inception module 0.941 0.894 0.907 Baumgartner et al (2017) 2D U-net with cross entropy loss 0.937 0.897 0.908 Zotti et al (2017) 2D GridNet with registered shape prior 0.931 0.890 0.912 Jang et al (2017) 2D M-Net with weighted cross entropy loss 0.940 0.885 0.907 Painchaud et al (2019) FCN followed by an AE for shape correction 0.936 0.889 0.909 Wolterink et al (2017c) Multi-input 2D dilated FCN, segmenting paired ED and ES frames simultaneously 0.940 0.885 0.900 Patravali et al (2017) 2D U-net with a Dice loss 0.920 0.890 0.865 Rohé et al (2017) Multi-atlas based method combined with 3D CNN for registration 0.929 0.868 0.881 Tziritas and Grinias (2017) Level-set +markov random field (MRF); Non-deep learning method 0.907 0.798 0.803 Yang et al (2017c) 3D FCN with deep supervision 0.820 N/A 0.780…”

Section: Scar Segmentationmentioning

confidence: 99%

Deep Learning for Cardiac Image Segmentation: A Review

Chen

Qiu

et al. 2020

Front. Cardiovasc. Med.

654

383

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Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research.

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Automatic Segmentation of LV and RV in Cardiac MRI

Cited by 49 publications

References 11 publications

An iterative multi‐path fully convolutional neural network for automatic cardiac segmentation in cine MR images

An iterative multi‐path fully convolutional neural network for automatic cardiac segmentation in cine MR images

Deep Learning Techniques for Automatic MRI Cardiac Multi-Structures Segmentation and Diagnosis: Is the Problem Solved?

Deep Learning for Cardiac Image Segmentation: A Review

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