Multi-modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images

Zhang, Zhen; Liu, Chenyu; Ding, Wangbin; Wang, Sihan; Pei, Chenhao; Yang, Ming‐Jen; Huang, Liqin

doi:10.1007/978-3-030-65651-5_4

Cited by 13 publications

(7 citation statements)

References 20 publications

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“…To reduce the complexity from background, all the teams cropped ROIs from the original images prior to the MyoPS. For example, USTB cropped ROIs of 256×256 pixel (Yu et al, 2020), and FZU cropped a small ROI and resized into images of 128×128 pixel (Zhang et al, 2020c). Another method was to perform a coarse segmentation on the images, to localize the position of LV, and then extract ROIs automatically.…”

Section: Preprocessingmentioning

confidence: 99%

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Li¹,

Wu²,

Wang³

et al. 2022

Preprint

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Assessment of myocardial viability is essential in diagnosis and treatment management of patients suffering from myocardial infarction, and classification of pathology on myocardium is the key to this assessment. This work defines a new task of medical image analysis, i.e., to perform myocardial pathology segmentation (MyoPS) combining threesequence cardiac magnetic resonance (CMR) images, which was first proposed in the MyoPS challenge, in conjunction with MICCAI 2020. The challenge provided 45 paired and pre-aligned CMR images, allowing algorithms to combine the complementary information from the three CMR sequences for pathology segmentation. In this article, we provide details of the challenge, survey the works from fifteen participants and interpret their methods according to five aspects, i.e., preprocessing, data augmentation, learning strategy, model architecture and post-processing. In addition, we analyze the results with respect to different factors, in order to examine the key obstacles and explore potential of solutions, as well as to provide a benchmark for future research. We conclude that while promising results have been reported, the research is still in the early stage, and more in-depth exploration is needed before a successful application to the clinics. Note that MyoPS data and evaluation tool continue to be publicly available upon registration via its homepage (www.sdspeople.fudan.edu.cn/zhuangxiahai/0/myops20/).

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Section: Preprocessingmentioning

confidence: 99%

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

Li¹,

Wu²,

Wang³

et al. 2022

Preprint

Self Cite

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“…In their work, multi-modality images are fused channel by channel as the multi-channel inputs to learn a fused feature representation but neglect to fully exploit the multi-modal features. Zhang et al proposed two cascade network, one is anatomical structure segmentation network, the other is pathological region segmentation network, modality specific feature fused by channel attention block with layer-level fusion strategy [53], this framework achieves good result but requires the image to go through segmentation network twice to get the final result. Zhang et al trained three parallel segmentation networks, and averaged the prediction with threshold 0.5 in the decision-level [51].…”

Section: Pathology Segmentationmentioning

confidence: 99%

TAUNet: a triple-attention-based multi-modality MRI fusion U-Net for cardiac pathology segmentation

Peng

Guo

et al. 2022

Complex Intell. Syst.

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Automated segmentation of cardiac pathology in MRI plays a significant role for diagnosis and treatment of some cardiac disease. In clinical practice, multi-modality MRI is widely used to improve the cardiac pathology segmentation, because it can provide multiple or complementary information. Recently, deep learning methods have presented implausible performance in multi-modality medical image segmentation. However, how to fuse the underlying multi-modality information effectively to segment the pathology with irregular shapes and small region at random locations, is still a challenge task. In this paper, a triple-attention-based multi-modality MRI fusion U-Net was proposed to learn complex relationship between different modalities and pay more attention on shape information, thus to achieve improved pathology segmentation. First, three independent encoders and one fusion encoder were applied to extract specific and multiple modality features. Secondly, we concatenate the modality feature maps and use the channel attention to fuse specific modal information at every stage of the three dedicate independent encoders, then the three single modality feature maps and channel attention feature maps are together concatenated to the decoder path. Spatial attention was adopted in decoder path to capture the correlation of various positions. Once more, we employ shape attention to focus shape-dependent information. Lastly, the training approach is made efficient by introducing deep supervision mechanism with object contextual representations block to ensure precisely boundary prediction. Our proposed network was evaluated on the public MICCAI 2020 Myocardial pathology segmentation dataset which involves patients suffering from myocardial infarction. Experiments on the dataset with three modalities demonstrate the effectiveness of fusion mode of our model, and attention mechanism can integrate various modality information well. We demonstrated that such a deep learning approach could better fuse complementary information to improve the segmentation performance of cardiac pathology.

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“…As for loss functions, most teams employ the Dice loss and the cross entropy loss. Also, FZU [25] utilizes boundary loss to enforce the model to focus on the boundary regions.…”

Section: Myops: Myocardial Pathology Segmentation From Multi-sequence...mentioning

confidence: 99%

Multi-Modality Cardiac Image Analysis with Deep Learning

Li¹,

Wu²,

Wang³

et al. 2021

Preprint

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Accurate cardiac computing, analysis and modeling from multi-modality images are important for the diagnosis and treatment of cardiac disease. Late gadolinium enhancement magnetic resonance imaging (LGE MRI) is a promising technique to visualize and quantify myocardial infarction (MI) and atrial scars. Automating quantification of MI and atrial scars can be challenging due to the low image quality and complex enhancement patterns of LGE MRI. Moreover, compared with the other sequences LGE MRIs with gold standard labels are particularly limited, which represents another obstacle for developing novel algorithms for automatic segmentation and quantification of LGE MRIs. This chapter aims to summarize the state-of-the-art and our recent advanced contributions on deep learning based multi-modality cardiac image analysis. Firstly, we introduce two benchmark works for multi-sequence cardiac MRI based myocardial and pathology segmentation. Secondly, two novel frameworks for left atrial scar segmentation and quantification from LGE MRI were presented. Thirdly, we present three unsupervised domain adaptation techniques for cross-modality cardiac image segmentation.

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Multi-modality Pathology Segmentation Framework: Application to Cardiac Magnetic Resonance Images

Cited by 13 publications

References 20 publications

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

MyoPS: A Benchmark of Myocardial Pathology Segmentation Combining Three-Sequence Cardiac Magnetic Resonance Images

TAUNet: a triple-attention-based multi-modality MRI fusion U-Net for cardiac pathology segmentation

Multi-Modality Cardiac Image Analysis with Deep Learning

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