MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , , , , , , , |
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| Format: | Preprint |
| Publié: |
2024
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| _version_ | 1866910820597760000 |
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| author | Zhou, Alexander Liu, Zelong Tieu, Andrew Patel, Nikhil Sun, Sean Yang, Anthony Choi, Peter Lee, Hao-Chih Tordjman, Mickael Deyer, Louisa Mei, Yunhao Fauveau, Valentin Soultanidis, George Taouli, Bachir Huang, Mingqian Doshi, Amish Fayad, Zahi A. Deyer, Timothy Mei, Xueyan |
| author_facet | Zhou, Alexander Liu, Zelong Tieu, Andrew Patel, Nikhil Sun, Sean Yang, Anthony Choi, Peter Lee, Hao-Chih Tordjman, Mickael Deyer, Louisa Mei, Yunhao Fauveau, Valentin Soultanidis, George Taouli, Bachir Huang, Mingqian Doshi, Amish Fayad, Zahi A. Deyer, Timothy Mei, Xueyan |
| contents | In this retrospective study, we annotated 44 structures on two datasets: an internal dataset of 1,518 MRI sequences from 843 patients at the Mount Sinai Health System, and an external dataset of 397 MRI sequences from 263 patients for benchmarking. The internal dataset trained the nnU-Net model MRAnnotator, which demonstrated strong generalizability on the external dataset. MRAnnotator outperformed existing models such as TotalSegmentator MRI and MRSegmentator on both datasets, achieving an overall average Dice score of 0.878 on the internal dataset and 0.875 on the external set. Model weights are available on GitHub, and the external test set can be shared upon request. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_01031 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures Zhou, Alexander Liu, Zelong Tieu, Andrew Patel, Nikhil Sun, Sean Yang, Anthony Choi, Peter Lee, Hao-Chih Tordjman, Mickael Deyer, Louisa Mei, Yunhao Fauveau, Valentin Soultanidis, George Taouli, Bachir Huang, Mingqian Doshi, Amish Fayad, Zahi A. Deyer, Timothy Mei, Xueyan Image and Video Processing Computer Vision and Pattern Recognition In this retrospective study, we annotated 44 structures on two datasets: an internal dataset of 1,518 MRI sequences from 843 patients at the Mount Sinai Health System, and an external dataset of 397 MRI sequences from 263 patients for benchmarking. The internal dataset trained the nnU-Net model MRAnnotator, which demonstrated strong generalizability on the external dataset. MRAnnotator outperformed existing models such as TotalSegmentator MRI and MRSegmentator on both datasets, achieving an overall average Dice score of 0.878 on the internal dataset and 0.875 on the external set. Model weights are available on GitHub, and the external test set can be shared upon request. |
| title | MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures |
| topic | Image and Video Processing Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2402.01031 |