# MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI

arXiv: https://arxiv.org/abs/2405.05944 · HTML: https://mlsanity.com/paper/2405.05944/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 31 references that are in the map.
- First posted on arXiv 9 May 2024.

## Builds on (most cited first, 14 of 14)

- [U-Net: Convolutional Networks for Biomedical Image Segmentation](https://mlsanity.com/paper/1505.04597/) (2015)
- [V-Net: Fully Convolutional Neural Networks for Volumetric Medical Image Segmentation](https://mlsanity.com/paper/1606.04797/) (2016)
- [UNETR: Transformers for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.10504/) (2021)
- [Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images](https://mlsanity.com/paper/2201.01266/) (2022)
- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [CoTr: Efficiently Bridging CNN and Transformer for 3D Medical Image Segmentation](https://mlsanity.com/paper/2103.03024/) (2021)
- [AbdomenCT-1K: Is Abdominal Organ Segmentation A Solved Problem?](https://mlsanity.com/paper/2010.14808/) (2020)
- [Evaluation of Algorithms for Multi-Modality Whole Heart Segmentation: An Open-Access Grand Challenge](https://mlsanity.com/paper/1902.07880/) (2019)
- [Robust machine learning segmentation for large-scale analysis of heterogeneous clinical brain MRI datasets](https://mlsanity.com/paper/2209.02032/) (2022)
- [Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports](https://mlsanity.com/paper/2306.08749/) (2023)
- [SegmentAnyBone: A Universal Model that Segments Any Bone at Any Location on MRI](https://mlsanity.com/paper/2401.12974/) (2024)
- [Lymph Node Detection in T2 MRI with Transformers](https://mlsanity.com/paper/2111.04885/) (2021)
- [Fully Automated Multi-Organ Segmentation in Abdominal Magnetic Resonance Imaging with Deep Neural Networks](https://mlsanity.com/paper/1912.11000/) (2019)
- [MRAnnotator: multi-Anatomy and many-Sequence MRI segmentation of 44 structures](https://mlsanity.com/paper/2402.01031/) (2024)

## Built on by (most cited first, 7 of 7)

- [VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank](https://mlsanity.com/paper/2406.00125/) (2024)
- [MRSegmentator: Multi-Modality Segmentation of 40 Classes in MRI and CT](https://mlsanity.com/paper/2405.06463/) (2024)
- [Leveraging Anatomical Priors for Automated Pancreas Segmentation on Abdominal CT](https://mlsanity.com/paper/2504.06921/) (2025)
- [Longitudinal Assessment of Lung Lesion Burden in CT](https://mlsanity.com/paper/2504.06924/) (2025)
- [Benchmarking Multi-Organ Segmentation Tools for Multi-Parametric T1-weighted Abdominal MRI](https://mlsanity.com/paper/2504.07729/) (2025)
- [Benchmarking of Deep Learning Methods for Generic MRI Multi-Organ Abdominal Segmentation](https://mlsanity.com/paper/2507.17971/) (2025)
- [In search of truth: Evaluating concordance of AI-based anatomy segmentation models](https://mlsanity.com/paper/2512.15921/) (2025)

## Abstract (arXiv)

Background: Segmentation of organs and structures in abdominal MRI is useful for many clinical applications, such as disease diagnosis and radiotherapy. Current approaches have focused on delineating a limited set of abdominal structures (13 types). To date, there is no publicly available abdominal MRI dataset with voxel-level annotations of multiple organs and structures. Consequently, a segmentation tool for multi-structure segmentation is also unavailable. Methods: We curated a T1-weighted abdominal MRI dataset consisting of 195 patients who underwent imaging at National Institutes of Health (NIH) Clinical Center. The dataset comprises of axial pre-contrast T1, arterial, venous, and delayed phases for each patient, thereby amounting to a total of 780 series (69,248 2D slices). Each series contains voxel-level annotations of 62 abdominal organs and structures. A 3D nnUNet model, dubbed as MRISegmentator-Abdomen (MRISegmentator in short), was trained on this dataset, and evaluation was conducted on an internal test set and two large external datasets: AMOS22 and Duke Liver. The predicted segmentations were compared against the ground-truth using the Dice Similarity Coefficient (DSC) and Normalized Surface Distance (NSD). Findings: MRISegmentator achieved an average DSC of 0.861±0.170 and a NSD of 0.924±0.163 in the internal test set. On the AMOS22 dataset, MRISegmentator attained an average DSC of 0.829±0.133 and a NSD of 0.908±0.067. For the Duke Liver dataset, an average DSC of 0.933±0.015 and a NSD of 0.929±0.021 was obtained. Interpretation: The proposed MRISegmentator provides automatic, accurate, and robust segmentations of 62 organs and structures in T1-weighted abdominal MRI sequences. The tool has the potential to accelerate research on various clinical topics, such as abnormality detection, radiotherapy, disease classification among others.