MM-WHS（多模态全心脏分割）是MICCAI 2017年举办的挑战赛，该数据集共包含120例多模态心脏影像，其中包含60例心脏CT/CTA和60例心脏MRI影像。这些影像涵盖了整个心脏及其重要子结构，均来自真实临床环境并用于临床诊断。由于影像来源多样，其质量参差不齐，部分影像质量相对较差，但这种多样性对于测试算法在真实临床环境中的鲁棒性至关重要。数据集分为训练集（含20例CT和20例MRI样本）和测试集（含40例CT和40例MRI样本）。对于训练集，提供了人工分割标注，包含左右心室腔、左右心房腔、左心室心肌、升主动脉和肺动脉等七大心脏子结构。

xinzang

MM-WHS 2017 Dataset
│
├── ct_train
│   ├── ct_train_1001_image.nii.gz
│   ├── ct_train_1001_label.nii.gz
│   ├── ct_train_1002_image.nii.gz
│   ├── ct_train_1002_label.nii.gz
│   └── ...
│
├── ct_test
│   ├── ct_test_2001_image.nii.gz
│   ├── ct_test_2002_image.nii.gz
│   └── ...
│
├── mr_train
│   ├── mr_train_1001_image.nii.gz
│   ├── mr_train_1001_label.nii.gz
│   ├── mr_train_1002_image.nii.gz
│   ├── mr_train_1002_label.nii.gz
│   └── ...
│
└── mr_test
    ├── mr_test_2001_image.nii.gz
    ├── mr_test_2002_image.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{9921323,
  author={Wu, Fuping and Zhuang, Xiahai},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={Minimizing Estimated Risks on Unlabeled Data: A New Formulation for Semi-Supervised Medical Image Segmentation}, 
  year={2023},
  volume={45},
  number={5},
  pages={6021-6036},
  doi={10.1109/TPAMI.2022.3215186}}

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发布日期： 2017