MyoPS 2020（多序列心脏磁共振心肌病理分割）是一个专注于心肌病理分割的数据集。该数据集提供了45例患者的对齐心脏T2/bSSFP/LGE多序列MRI影像数据，这些数据已对瘢痕、水肿、正常心肌以及左右心室血池进行了人工标注。挑战赛的目标是通过结合多序列CMR数据来分割心肌病理。数据集分为25例训练集和20例测试集，训练集的标签已公开提供，测试集可使用官方提供的评估工具包进行评估。

xinzang

MyoPS 2020 Dataset
│
├── MyoPS2020_EvaluateByYourself
│   ├── readme.txt
│   ├── zhxCardMyoPSEvaluate
│   ├── zhxCardMyoPSEvaluate.exe
│   └── test_data_gd
│       ├── myops_test_201_gdencrypt.nii.gz
│       ├── myops_test_202_gdencrypt.nii.gz
│       └── ...
│
├── test20
│   ├── myops_test_201_C0.nii.gz
│   ├── myops_test_201_DE.nii.gz
│   ├── myops_test_201_T2.nii.gz
│   ├── myops_test_202_C0.nii.gz
│   ├── myops_test_202_DE.nii.gz
│   ├── myops_test_202_T2.nii.gz
│   └── ...
│
├── train25
│   ├── myops_training_101_C0.nii.gz
│   ├── myops_training_101_DE.nii.gz
│   ├── myops_training_101_T2.nii.gz
│   ├── myops_training_102_C0.nii.gz
│   ├── myops_training_102_DE.nii.gz
│   ├── myops_training_102_T2.nii.gz
│   └── ...
│
└── train25_myops_gd
    ├── myops_training_101_gd.nii.gz
    ├── myops_training_102_gd.nii.gz
    └── ...

@article{GAO2023102889,
title = {BayeSeg: Bayesian modeling for medical image segmentation with interpretable generalizability},
journal = {Medical Image Analysis},
volume = {89},
pages = {102889},
year = {2023},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2023.102889},
url = {https://www.sciencedirect.com/science/article/pii/S1361841523001494},
author = {Shangqi Gao and Hangqi Zhou and Yibo Gao and Xiahai Zhuang},
keywords = {Image segmentation, Interpretation and generalization, Statistical modeling, Variational Bayes},
abstract = {Due to the cross-domain distribution shift aroused from diverse medical imaging systems, many deep learning segmentation methods fail to perform well on unseen data, which limits their real-world applicability. Recent works have shown the benefits of extracting domain-invariant representations on domain generalization. However, the interpretability of domain-invariant features remains a great challenge. To address this problem, we propose an interpretable Bayesian framework (BayeSeg) through Bayesian modeling of image and label statistics to enhance model generalizability for medical image segmentation. Specifically, we first decompose an image into a spatial-correlated variable and a spatial-variant variable, assigning hierarchical Bayesian priors to explicitly force them to model the domain-stable shape and domain-specific appearance information respectively. Then, we model the segmentation as a locally smooth variable only related to the shape. Finally, we develop a variational Bayesian framework to infer the posterior distributions of these explainable variables. The framework is implemented with neural networks, and thus is referred to as deep Bayesian segmentation. Quantitative and qualitative experimental results on prostate segmentation and cardiac segmentation tasks have shown the effectiveness of our proposed method. Moreover, we investigated the interpretability of BayeSeg by explaining the posteriors and analyzed certain factors that affect the generalization ability through further ablation studies. Our code is released via https://zmiclab.github.io/projects.html.}
}

@ARTICLE{8458220,
  author={Zhuang, Xiahai},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images}, 
  year={2019},
  volume={41},
  number={12},
  pages={2933-2946},
  doi={10.1109/TPAMI.2018.2869576}}

@ARTICLE{9965747,
  author={Luo, Xinzhe and Zhuang, Xiahai},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={$\mathcal {X}$-Metric: An N-Dimensional Information-Theoretic Framework for Groupwise Registration and Deep Combined Computing}, 
  year={2023},
  volume={45},
  number={7},
  pages={9206-9224},
  doi={10.1109/TPAMI.2022.3225418}}

@article{QIU2023102694,
title = {MyoPS-Net: Myocardial pathology segmentation with flexible combination of multi-sequence CMR images},
journal = {Medical Image Analysis},
volume = {84},
pages = {102694},
year = {2023},
issn = {1361-8415},
doi = {https://doi.org/10.1016/j.media.2022.102694},
url = {https://www.sciencedirect.com/science/article/pii/S136184152200322X},
author = {Junyi Qiu and Lei Li and Sihan Wang and Ke Zhang and Yinyin Chen and Shan Yang and Xiahai Zhuang},
keywords = {Multi-sequence CMR, Myocardial pathology segmentation, Missing modality, Practical clinics},
abstract = {Myocardial pathology segmentation (MyoPS) can be a prerequisite for the accurate diagnosis and treatment planning of myocardial infarction. However, achieving this segmentation is challenging, mainly due to the inadequate and indistinct information from an image. In this work, we develop an end-to-end deep neural network, referred to as MyoPS-Net, to flexibly combine five-sequence cardiac magnetic resonance (CMR) images for MyoPS. To extract precise and adequate information, we design an effective yet flexible architecture to extract and fuse cross-modal features. This architecture can tackle different numbers of CMR images and complex combinations of modalities, with output branches targeting specific pathologies. To impose anatomical knowledge on the segmentation results, we first propose a module to regularize myocardium consistency and localize the pathologies, and then introduce an inclusiveness loss to utilize relations between myocardial scars and edema. We evaluated the proposed MyoPS-Net on two datasets, i.e., a private one consisting of 50 paired multi-sequence CMR images and a public one from MICCAI2020 MyoPS Challenge. Experimental results showed that MyoPS-Net could achieve state-of-the-art performance in various scenarios. Note that in practical clinics, the subjects may not have full sequences, such as missing LGE CMR or mapping CMR scans. We therefore conducted extensive experiments to investigate the performance of the proposed method in dealing with such complex combinations of different CMR sequences. Results proved the superiority and generalizability of MyoPS-Net, and more importantly, indicated a practical clinical application. The code has been released via https://github.com/QJYBall/MyoPS-Net.}
}

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发布日期： 2020-04