MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures
This work addresses MRI segmentation for medical imaging applications, but it is incremental as it applies an existing method (nnU-Net) to new data.
The study tackled multi-anatomy MRI segmentation of 44 structures by training the nnU-Net model MRAnnotator on an internal dataset, achieving an overall average Dice score of 0.878 on the internal dataset and 0.875 on an external benchmark.
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.