Pancakes: Consistent Multi-Protocol Image Segmentation Across Biomedical Domains

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.