# Benchmarking Multi-Organ Segmentation Tools for Multi-Parametric T1-weighted Abdominal MRI

arXiv: https://arxiv.org/abs/2504.07729 · HTML: https://mlsanity.com/paper/2504.07729/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 6 of its 28 references that are in the map.
- First posted on arXiv 10 Apr 2025.

## Builds on (most cited first, 6 of 6)

- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [AMOS: A Large-Scale Abdominal Multi-Organ Benchmark for Versatile Medical Image Segmentation](https://mlsanity.com/paper/2206.08023/) (2022)
- [Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports](https://mlsanity.com/paper/2306.08749/) (2023)
- [Improving Robustness of Deep Learning Based Knee MRI Segmentation: Mixup and Adversarial Domain Adaptation](https://mlsanity.com/paper/1908.04126/) (2019)
- [MRISegmentator-Abdomen: A Fully Automated Multi-Organ and Structure Segmentation Tool for T1-weighted Abdominal MRI](https://mlsanity.com/paper/2405.05944/) (2024)
- [Enhanced Muscle and Fat Segmentation for CT-Based Body Composition Analysis: A Comparative Study](https://mlsanity.com/paper/2401.05294/) (2024)

## Abstract (arXiv)

The segmentation of multiple organs in multi-parametric MRI studies is critical for many applications in radiology, such as correlating imaging biomarkers with disease status (e.g., cirrhosis, diabetes). Recently, three publicly available tools, such as MRSegmentator (MRSeg), TotalSegmentator MRI (TS), and TotalVibeSegmentator (VIBE), have been proposed for multi-organ segmentation in MRI. However, the performance of these tools on specific MRI sequence types has not yet been quantified. In this work, a subset of 40 volumes from the public Duke Liver Dataset was curated. The curated dataset contained 10 volumes each from the pre-contrast fat saturated T1, arterial T1w, venous T1w, and delayed T1w phases, respectively. Ten abdominal structures were manually annotated in these volumes. Next, the performance of the three public tools was benchmarked on this curated dataset. The results indicated that MRSeg obtained a Dice score of 80.7 ± 18.6 and Hausdorff Distance (HD) error of 8.9 ± 10.4 mm. It fared the best (p < .05) across the different sequence types in contrast to TS and VIBE.