mlsanityData to 14 Oct 2026

Zero-shot Domain Generalization of Foundational Models for 3D Medical Image Segmentation: An Experimental Study

arXiv 2503.22862 ↗First posted 28 Mar 2025cs.CVcited by 3

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Abstract (arXiv)

Domain shift, caused by variations in imaging modalities and acquisition protocols, limits model generalization in medical image segmentation. While foundation models (FMs) trained on diverse large-scale data hold promise for zero-shot generalization, their application to volumetric medical data remains underexplored. In this study, we examine their ability towards domain generalization (DG), by conducting a comprehensive experimental study encompassing 6 medical segmentation FMs and 12 public datasets spanning multiple modalities and anatomies. Our findings reveal the potential of promptable FMs in bridging the domain gap via smart prompting techniques. Additionally, by probing into multiple facets of zero-shot DG, we offer valuable insights into the viability of FMs for DG and identify promising avenues for future research.

Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). Builds on 35 of its 48 references that are in the map. First posted on arXiv 28 Mar 2025.

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35 of 48 references.

References · 35

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23 more
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2025: 1; 2026: 2.

3 papers.

  1. 2026On The Robustness of Foundational 3D Medical Image Segmentation Models Against Imprecise Visual Promptscited by 2
  2. 2025Make me an Expert: Distilling from Generalist Black-Box Models into Specialized Models for Semantic Segmentationcited by 0
  3. 2026Are Natural-Domain Foundation Models Effective for Accelerated Cardiac MRI Reconstruction?cited by 0