Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains
Marianne Rakic, Siyu Gai, Etienne Chollet, John Guttag, Adrian V Dalca
Abstract
A single biomedical image can be segmented in multiple valid ways, depending on the application. For instance, a brain MRI may be divided according to tissue types, vascular territories, broad anatomical regions, fine-grained anatomy, or pathology. Existing automatic segmentation models typically either (1) support only a single protocol---the one they were trained on---or (2) require labor-intensive prompting to specify the desired segmentation. We introduce _Pancakes_, a framework that, given a new image from a previously unseen domain, automatically generates multi-label segmentation maps for _multiple_ plausible protocols, while maintaining semantic consistency across related images. In extensive experiments across seven previously unseen domains, _Pancakes_ consistently outperforms strong baselines, often by a wide margin, demonstrating its ability to produce diverse yet coherent segmentation maps on unseen domains.
BibTeX
@inproceedings{
rakic2025pancakes,
title={Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains},
author={Marianne Rakic and Siyu Gai and Etienne Chollet and John Guttag and Adrian V Dalca},
booktitle={The Thirty-ninth Annual Conference on Neural Information Processing Systems},
year={2025},
url={https://openreview.net/forum?id=9ednYuGHN1}
}