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MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures

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arxiv 2402.01031 v2 pith:PNRWEZTK submitted 2024-02-01 eess.IV cs.CV

MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures

Alexander Zhou , Zelong Liu , Andrew Tieu , Nikhil Patel , Sean Sun , Anthony Yang , Peter Choi , Hao-Chih Lee
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Mickael Tordjman Louisa Deyer Yunhao Mei Valentin Fauveau George Soultanidis Bachir Taouli Mingqian Huang Amish Doshi Zahi A. Fayad Timothy Deyer Xueyan Mei
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classification eess.IV cs.CV
keywords datasetexternalinternalmrannotatordatasetsmodelpatientssequences
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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.

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