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Interactive Foundation Models for 3D MR Uterus Segmentation: Performance and Limitations

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Julia Schnabel

Prof. Dr.

Core PI

Computational Imaging and AI in Medicine

Abstract

Magnetic Resonance Imaging (MRI) plays a crucial role in the investigation of the uterus, allowing for detailed visualization and segmentation of anatomical structures. In this work, we tested interactive segmentation foundation models for the segmentation of the uterine wall and cavity. We apply and evaluate VoxTell, MedSAM2D, and nnInteractive using text-, box-, and point-based prompts, and compare them to nnU-Net. For repeatability and reproducibility, we use the Ground Truth (GT) for prompt creation and propose a five-point strategy for multi-point prompting. Our results show that nnU-Net outperforms the interactive foundation models in terms of Dice similarity coefficient and Hausdorff distance. Additional analyses show that performance decreases with lesion volume, distance from the prompt, and spatial resolution. We further analyze the behavior of bounding box and multi-point prompts, showing that the proposed sampling strategy outperforms random point selection. Overall, our results highlight key limitations of current interactive foundation models and identify factors that make uterine segmentation particularly challenging.

inproceedings GFR+26

CAPI-WOMEN @MICCAI 2026

Joint Computer-Assisted Pelvic Imaging (CAPI) and Women’s Health (WOMEN) Workshops at the 29th International Conference on Medical Image Computing and Computer Assisted Intervention. Strasbourg, France, Sep 27-Oct 01, 2026. To be published. Preprint available.

Authors

C. Grange • S. M. Fischer • A. Reithmeir • J. A. Schnabel • L. Felsner

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Research Area

 C1 | Medicine

BibTeXKey: GFR+26

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