Multi-scale patch and multi-modality atlases for whole heart segmentation of MRI

By Xiahai Zhuang, Juan Shen. Medical Image Anal., 2016

512 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/bf160d3a2974d996fa93d71ee5cd6264d86ff42e

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In Computer Vision and Pattern Recognition

1. Holger R. Roth
2. P. Heng
3. Qi-Yan Dou
4. Daguang Xu
5. Hao Chen
6. Jing Qin
7. Dong Yang
8. Cheng Chen

Citations in the corpus

• Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation (2020, methodology): “1) Cardiac Substructure Segmentation: We employed the Multi-Modality Whole Heart Segmentation (MMWHS) Challenge 2017 dataset for cardiac segmentation [47].”
• Disentangled representation learning in cardiac image analysis (2019, methodology): “ction 5.3), as well as modality estimation (Section 5.4), we use data from the 2017 Multi-Modal Whole Heart Segmentation (MM-WHS) challenge, made available by Zhuang et al. (2010), Zhuang (2013), and Zhuang and Shen (2016). This contains 40 anonymised volumes, of which 20 are cardiac CT/CT angiograp”
• Learning with Limited Annotations: A Survey on Deep Semi-Supervised Learning for Medical Image Segmentation (2022, methodology): “[110] 3D CT MM-WHS dataset [96], [97] Different transformations”
• Multivariate Mixture Model for Myocardial Segmentation Combining Multi-Source Images (2016, methodology): “The MvMM is initialized and regularized by the prior probabilities from an atlas which can be registered to the common space of the target images using the conventional methods in the atlas-based segmentation framework [28].”
• Deep Unsupervised Domain Adaptation: A Review of Recent Advances and Perspectives (2022, methodology): “The popular evaluation setting is to use the MMWHS challenge dataset as the source domain and the MMAS dataset as the target domain, respectively (Zhuang and Shen, 2016).”
• Generalizing Deep Learning for Medical Image Segmentation to Unseen Domains via Deep Stacked Transformation (2020, methodology): “Task 2: Three publicly available 3D heart MRI datasets are used: MSD-Heart (MSD-H) 2 , 2018 ASC, 6 and MM-WHS 7 [38].”
• Contrastive learning of global and local features for medical image segmentation with limited annotations (2020, methodology): “[III] The MMWHS dataset was hosted in STACOM and MICCAI 2017 challenge [69, 70, 68, 67, 3].”
• Domain Adaptation for Medical Image Analysis: A Survey (2021, unknown): “In [54], [55], the authors propose a domain transfer support vector machine (DTSVM) for mild cognitive impairment (MCI) classification.”