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arXiv · 2512.13534

Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains

Marianne Rakic·Siyu Gai·Etienne Chollet·John V. Guttag·Adrian V. Dalca
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Abstract

A single biomedical image can be meaningfully segmented in multiple ways, depending on the desired application. For instance, a brain MRI can be segmented according to tissue types, vascular territories, broad anatomical regions, fine-grained anatomy, or pathology, etc. Existing automatic segmentation models typically either (1) support only a single protocol, the one they were trained on, or (2) require labor-intensive manual 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. Pancakes introduces a new problem formulation that is not currently attainable by existing foundation models. In a series of experiments on seven held-out datasets, we demonstrate that our model can significantly outperform existing foundation models in producing several plausible whole-image segmentations, that are semantically coherent across images.

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Marianne Rakic, Siyu Gai, Etienne Chollet, John V. Guttag, Adrian V. Dalca. 2025-12-15. Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains. https://arxiv.org/abs/2512.13534

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