Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

Zhuang, Xiahai; Li, Lei; Payer, Christian; Štern, Darko; Urschler, Martin; Heinrich, Mattias P.; Oster, Julien; Wang, Chunliang; Smedby, Örjan; Bian, Cheng; Yang, Xin; Heng, Pheng‐Ann; Mortazi, Aliasghar; Bağcı, Ulaş; Yang, Guangbin; Sun, Chenchen; Galisot, Gaëtan; Ramel, Jean-Yves; Brouard, Thierry; Tong, Qianqian; Si, Weixin; Liao, Xiangyun; Zeng, Guodong; Shi, Zenglin; Zheng, Guoyan; Wang, Chengjia; MacGillivray, Tom; Newby, David E.; Rhode, Kawal; Ourselin, Sébastien; Mohiaddin, Raad H.; Keegan, Jennifer; Firmin, David N.; Yang, Guang

doi:10.1016/j.media.2019.101537

Cited by 239 publications

(214 citation statements)

References 48 publications

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“…We compare the performance of different multi-modal learning methods, including two state-of-the-art approaches [9], [15]. We also refer to the available winning performance of the challenge [36], [38] to demonstrate effectiveness of multi-modal learning.…”

Section: Segmentation Results and Comparison With State-of-the-artsmentioning

confidence: 99%

“…Task-1: We perform multi-class cardiac structure segmentation using the MICCAI 2017 Multi-Modality Whole Heart Segmentation Challenge [36] dataset, which consists of unpaired 20 CT and 20 MRI images from different patients and sites. The multi-class segmentation includes four structures: left ventricle myocardium (LVM), left atrium blood cavity (LAC), left ventricle blood cavity (LVC) and ascending aorta (AA).…”

Section: A Datasets and Networkmentioning

confidence: 99%

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Unpaired Multi-Modal Segmentation via Knowledge Distillation

Dou

Liu

Heng

et al. 2020

IEEE Trans. Med. Imaging

151

100

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Multi-modal learning is typically performed with network architectures containing modality-specific layers and shared layers, utilizing co-registered images of different modalities. We propose a novel learning scheme for unpaired crossmodality image segmentation, with a highly compact architecture achieving superior segmentation accuracy. In our method, we heavily reuse network parameters, by sharing all convolutional kernels across CT and MRI, and only employ modality-specific internal normalization layers which compute respective statistics.To effectively train such a highly compact model, we introduce a novel loss term inspired by knowledge distillation, by explicitly constraining the KL-divergence of our derived prediction distributions between modalities. We have extensively validated our approach on two multi-class segmentation problems: i) cardiac structure segmentation, and ii) abdominal organ segmentation. Different network settings, i.e., 2D dilated network and 3D Unet, are utilized to investigate our method's general efficacy. Experimental results on both tasks demonstrate that our novel multi-modal learning scheme consistently outperforms singlemodal training and previous multi-modal approaches.

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Section: Segmentation Results and Comparison With State-of-the-artsmentioning

confidence: 99%

Section: A Datasets and Networkmentioning

confidence: 99%

Unpaired Multi-Modal Segmentation via Knowledge Distillation

Dou

Liu

Heng

et al. 2020

IEEE Trans. Med. Imaging

151

100

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show abstract

“…For instance, Zreik et al (2016) proposed a two-step LV segmentation process where a bounding box for the LV is first detected using the method described Table 4. For more details, please refer to Zhuang et al (2019).…”

Section: Cardiac Substructure Segmentationmentioning

confidence: 99%

“…Reported numbers are Dice scores (CT/MRI) for different substructures on both CT and MRI scans. For more detailed comparisons, please refer toZhuang et al (2019).…”

mentioning

confidence: 99%

Deep Learning for Cardiac Image Segmentation: A Review

Chen

Qiu

et al. 2020

Front. Cardiovasc. Med.

607

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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“…More recently, convolutional neural networks (CNN) based approaches have been proposed to segment the LA and PV [12] [13] [14][15] [16] and a grand challenge has been held for LA anatomy segmentation [17]. These research studies on LA anatomy segmentation can potentially be useful for LA scars segmentation although to the best of our knowledge, this has not been done to date.…”

Section: Segmentation Of the La Anatomymentioning

confidence: 99%

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

Yang

Chen

Gao

et al. 2020

Future Generation Computer Systems

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Three-dimensional late gadolinium enhanced (LGE) cardiac MR (CMR) of left atrial scar in patients with atrial fibrillation (AF) has recently emerged as a promising technique to stratify patients, to guide ablation therapy and to predict treatment success. This requires a segmentation of the high intensity scar tissue and also a segmentation of the left atrium (LA) anatomy, the latter usually being derived from a separate bright-blood acquisition. Performing both segmentations automatically from a single 3D LGE CMR acquisition would eliminate the need for an additional acquisition and avoid subsequent registration issues. In this paper, we propose a joint segmentation method based on multiview two-task (MVTT) recursive attention model working directly on 3DLGE CMR images to segment the LA (and proximal pulmonary veins) and to delineate the scar on the same dataset. Using our MVTT recursive attention model, both the LA anatomy and scar can be segmented accurately (mean Dice score of 93% for the LA anatomy and 87% for the scar segmentations) and efficiently (~0.27 seconds to simultaneously segment the LA anatomy and scars directly from the 3D LGE CMR dataset with 60-68 2D slices). Compared to conventional unsupervised learning and other state-of-the-art deep learning based methods, the proposed MVTT model achieved excellent results, leading to an automatic generation of a patient-specific anatomical model combined with scar segmentation for patients in AF.

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Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge

Cited by 239 publications

References 48 publications

Unpaired Multi-Modal Segmentation via Knowledge Distillation

Unpaired Multi-Modal Segmentation via Knowledge Distillation

Deep Learning for Cardiac Image Segmentation: A Review

Simultaneous left atrium anatomy and scar segmentations via deep learning in multiview information with attention

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