用量子桥接模型实现细胞实例分割，无需后处理即可稳定出图
Cell Instance Segmentation via Multi-Task Image-to-Image Schrödinger Bridge
- 将分割任务转为分布生成问题，利用反向距离图强化边界监督
- 在PanNuke上性能媲美或超越现有方法，无需SAM预训练
- 对小样本数据鲁棒，适合医学图像中数据稀缺场景
现有细胞实例分割方法通常结合确定性预测与后处理，对实例掩码的全局结构约束有限。本文提出一种多任务图像到图像的薛定谔桥框架，将实例分割建模为基于分布的图像到图像生成问题。通过反向距离图引入边界感知监督，并采用确定性推理以生成稳定预测。在PanNuke数据集上的实验表明，该方法在不依赖SAM预训练或额外后处理的情况下，实现了具有竞争力或更优的性能。在MoNuSeg数据集上的附加结果表明，在训练数据有限时仍具备强鲁棒性。这些发现表明，基于薛定谔桥的图像到图像生成为细胞实例分割提供了一个有效框架。
原文摘要 · Abstract (English)
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.
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