mlsanityData to 7 Oct 2026

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

arXiv 2402.01031 ↗First posted 1 Feb 2024eess.IV cs.CVcited by 3

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Abstract (arXiv)

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.

Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). Builds on 10 of its 30 references that are in the map. First posted on arXiv 1 Feb 2024.

Builds on

10 of 30 references.

References · 10

  1. 2022TotalSegmentator: robust segmentation of 104 anatomical structures in CT imagescited by 597
  2. 2022AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentationcited by 347
  3. 2020CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentationcited by 307
  4. 2018An overview of deep learning in medical imaging focusing on MRIcited by 140
  5. 2020nnU-Net for Brain Tumor Segmentationcited by 103
  6. 2018Deep learning in radiology: an overview of the concepts and a survey of the state of the artcited by 34
  7. 2022Few-shot Medical Image Segmentation with Cycle-resemblance Attentioncited by 33
  8. 2022Prototypical few-shot segmentation for cross-institution male pelvic structures with spatial registrationcited by 14
  9. 2022Fighting the scanner effect in brain MRI segmentation with a progressive level-of-detail network trained on multi-site datacited by 5
  10. 2022Multi-Modality Abdominal Multi-Organ Segmentation with Deep Supervised 3D Segmentation Modelcited by 1

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Cited by year

Citing papers per year: 2024: 2; 2025: 1.

3 papers.

  1. 2024MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRIcited by 7
  2. 2024MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CTcited by 6
  3. 2025Adapting Medical Vision Foundation Models for Volumetric Medical Image Segmentation via Active Learning and Selective Semi-supervised Fine-tuningcited by 0