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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2402.01031 (eess)
[Submitted on 1 Feb 2024 (v1), last revised 11 Feb 2025 (this version, v2)]

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

Authors:Alexander Zhou, Zelong Liu, Andrew Tieu, Nikhil Patel, Sean Sun, Anthony Yang, Peter Choi, Hao-Chih Lee, 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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Abstract: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.
Subjects: Image and Video Processing (eess.IV); Computer Vision and Pattern Recognition (cs.CV)
Cite as: arXiv:2402.01031 [eess.IV]
  (or arXiv:2402.01031v2 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2402.01031
Journal reference: Radiology Advances, Volume 2, Issue 1, January 2025
Related DOI: https://doi.org/10.1093/radadv/umae035

Submission history

From: Xueyan Mei [view email]
[v1] Thu, 1 Feb 2024 21:43:27 UTC (303 KB)
[v2] Tue, 11 Feb 2025 16:23:09 UTC (864 KB)
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