Cell Instance Segmentation via Multi-Task Image-to-Image Schrödinger Bridge
It offers a new distribution-based approach to cell instance segmentation that avoids complex post-processing, benefiting biomedical image analysis.
The paper proposes a multi-task image-to-image Schrödinger Bridge framework for cell instance segmentation, achieving competitive or superior performance on PanNuke and MoNuSeg datasets without SAM pre-training or post-processing.
Existing cell instance segmentation pipelines typically combine deterministic predictions with post-processing, which imposes limited explicit constraints on the global structure of instance masks. In this work, we propose a multi-task image-to-image Schrödinger Bridge framework that formulates instance segmentation as a distribution-based image-to-image generation problem. Boundary-aware supervision is integrated through a reverse distance map, and deterministic inference is employed to produce stable predictions. Experimental results on the PanNuke dataset demonstrate that the proposed method achieves competitive or superior performance without relying on SAM pre-training or additional post-processing. Additional results on the MoNuSeg dataset show robustness under limited training data. These findings indicate that Schrödinger Bridge-based image-to-image generation provides an effective framework for cell instance segmentation.