# TotalSegmentator MRI: Robust Sequence-independent Segmentation of Multiple Anatomic Structures in MRI

arXiv: https://arxiv.org/abs/2405.19492 · HTML: https://mlsanity.com/paper/2405.19492/

## Key facts

- Cited by 59 papers in the map (data through 14 Oct 2026).
- Builds on 5 of its 16 references that are in the map.
- First posted on arXiv 29 May 2024.

## Builds on (most cited first, 5 of 5)

- [TotalSegmentator: robust segmentation of 104 anatomical structures in CT images](https://mlsanity.com/paper/2208.05868/) (2022)
- [CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation](https://mlsanity.com/paper/2001.06535/) (2020)
- [Segment Anything in Medical Images](https://mlsanity.com/paper/2304.12306/) (2023)
- [Metrics reloaded: Recommendations for image analysis validation](https://mlsanity.com/paper/2206.01653/) (2022)
- [SynthStrip: Skull-Stripping for Any Brain Image](https://mlsanity.com/paper/2203.09974/) (2022)

## Built on by (most cited first, 30 of 59)

- [A Survey of Scientific Large Language Models: From Data Foundations to Agent Frontiers](https://mlsanity.com/paper/2508.21148/) (2025)
- [VIBESegmentator: Full Body MRI Segmentation for the NAKO and UK Biobank](https://mlsanity.com/paper/2406.00125/) (2024)
- [Beyond the Hype: A dispassionate look at vision-language models in medical scenario](https://mlsanity.com/paper/2408.08704/) (2024)
- [DrVD-Bench: Do Vision-Language Models Reason Like Human Doctors in Medical Image Diagnosis?](https://mlsanity.com/paper/2505.24173/) (2025)
- [Curia-2: Scaling Self-Supervised Learning for Radiology Foundation Models](https://mlsanity.com/paper/2604.01987/) (2026)
- [MIST: A Simple and Scalable End-To-End 3D Medical Imaging Segmentation Framework](https://mlsanity.com/paper/2407.21343/) (2024)
- [MedNeXt-v2: Scaling 3D ConvNeXts for Large-Scale Supervised Representation Learning in Medical Image Segmentation](https://mlsanity.com/paper/2512.17774/) (2025)
- [MCP-MedSAM: A Powerful Lightweight Medical Segment Anything Model Trained with a Single GPU in Just One Day](https://mlsanity.com/paper/2412.05888/) (2024)
- [A Comprehensive Analysis of Mamba for 3D Volumetric Medical Image Segmentation](https://mlsanity.com/paper/2503.19308/) (2025)
- [UKBOB: One Billion MRI Labeled Masks for Generalizable 3D Medical Image Segmentation](https://mlsanity.com/paper/2504.06908/) (2025)
- [MRGen: Segmentation Data Engine for Underrepresented MRI Modalities](https://mlsanity.com/paper/2412.04106/) (2024)
- [Mamba Goes HoME: Hierarchical Soft Mixture-of-Experts for 3D Medical Image Segmentation](https://mlsanity.com/paper/2507.06363/) (2025)
- [Large-scale Multi-sequence Pretraining for Generalizable MRI Analysis in Versatile Clinical Applications](https://mlsanity.com/paper/2508.07165/) (2025)
- [MedGS: Gaussian Splatting for Multi-Modal 3D Medical Imaging](https://mlsanity.com/paper/2509.16806/) (2025)
- [Deep learning-based neurodevelopmental assessment in preterm infants](https://mlsanity.com/paper/2601.11944/) (2026)
- [A weakly-supervised deep learning model for fast localisation and delineation of the skeleton, internal organs, and spinal canal on Whole-Body Diffusion-Weighted MRI (WB-DWI)](https://mlsanity.com/paper/2503.20722/) (2025)
- [Generalist Models in Medical Image Segmentation: A Survey and Performance Comparison with Task-Specific Approaches](https://mlsanity.com/paper/2506.10825/) (2025)
- [The Missing Piece: A Case for Pre-Training in 3D Medical Object Detection](https://mlsanity.com/paper/2509.15947/) (2025)
- [SAMRI: Segment Any MRI](https://mlsanity.com/paper/2510.26635/) (2025)
- [BackSplit: The Importance of Sub-dividing the Background in Biomedical Lesion Segmentation](https://mlsanity.com/paper/2511.19394/) (2025)
- [AnyCXR: Human Anatomy Segmentation of Chest X-ray at Any Acquisition Position using Multi-stage Domain Randomized Synthetic Data with Imperfect Annotations and Conditional Joint Annotation Regularization Learning](https://mlsanity.com/paper/2512.17263/) (2025)
- [Med-Scout: Curing MLLMs' Geometric Blindness in Medical Perception via Geometry-Aware RL Post-Training](https://mlsanity.com/paper/2601.23220/) (2026)
- [Generating synthetic computed tomography for radiotherapy: SynthRAD2025 challenge report](https://mlsanity.com/paper/2605.13555/) (2026)
- [KA2ER: Knowledge Adaptive Amalgamation of ExpeRts for Medical Images Segmentation](https://mlsanity.com/paper/2410.21085/) (2024)
- [SegmentAnyMuscle: A universal muscle segmentation model across different locations in MRI](https://mlsanity.com/paper/2506.22467/) (2025)
- [BreastSegNet: Multi-label Segmentation of Breast MRI](https://mlsanity.com/paper/2507.13604/) (2025)
- [Towards MR-Based Trochleoplasty Planning](https://mlsanity.com/paper/2508.06076/) (2025)
- [Lifespan Pancreas Morphology for Control vs Type 2 Diabetes using AI on Largescale Clinical Imaging](https://mlsanity.com/paper/2508.14878/) (2025)
- [DuoLift-GAN:Reconstructing CT from Single-view and Biplanar X-Rays with Generative Adversarial Networks](https://mlsanity.com/paper/2411.07941/) (2024)
- [Modality-Projection Universal Model for Comprehensive Full-Body Medical Imaging Segmentation](https://mlsanity.com/paper/2412.19026/) (2024)

## Abstract (arXiv)

Since the introduction of TotalSegmentator CT, there is demand for a similar robust automated MRI segmentation tool that can be applied across all MRI sequences and anatomic structures. In this retrospective study, a nnU-Net model (TotalSegmentator) was trained on MRI and CT examinations to segment 80 anatomic structures relevant for use cases such as organ volumetry, disease characterization, surgical planning and opportunistic screening. Examinations were randomly sampled from routine clinical studies to represent real-world examples. Dice scores were calculated between the predicted segmentations and expert radiologist reference standard segmentations to evaluate model performance on an internal test set, two external test sets and against two publicly available models, and TotalSegmentator CT. The model was applied to an internal dataset containing abdominal MRIs to investigate age-dependent volume changes. A total of 1143 examinations (616 MRIs, 527 CTs) (median age 61 years, IQR 50-72) were split into training (n=1088, CT and MRI) and an internal test set (n=55; only MRI), two external test sets (AMOS, n=20; CHAOS, n=20; only MRI), and an internal aging-study dataset of 8672 abdominal MRIs (median age 59 years, IQR 45-70) were included. The model showed a Dice Score of 0.839 on the internal test set and outperformed two other models (Dice Score, 0.862 versus 0.759; and 0.838 versus 0.560; p<.001 for both). The proposed open-source, easy-to-use model allows for automatic, robust segmentation of 80 structures, extending the capabilities of TotalSegmentator to MRIs of any sequence. The ready-to-use online tool is available at https://totalsegmentator.com, the model at https://github.com/wasserth/TotalSegmentator, and the dataset at https://zenodo.org/records/14710732.