通过模拟细胞迁移生成更丰富的病理图像，提升有限标注下的分割效果。
Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology
- 设计细胞迁移模块与结构感知修复模块，生成多样核布局与空间关系。
- 在CoNSeP和GLySAC数据集上，生成图像使实例分割性能显著提升。
- 适合数据稀缺场景下的病理图像增强，尤其适用于细胞分割任务。
核实例分割在数字病理学疾病诊断中至关重要，但病理图像标注数据有限，制约了分割性能。为此，我们提出一种新型数据增强框架——实例迁移扩散模型（IM-Diffusion），通过构建多样的核布局和核间空间关系，生成更多样化的病理图像。具体而言，引入核迁移模块（NMM），模拟核迁移过程生成多样化核分布；在此基础上，提出核间区域修复模块（IIM），通过结构感知修复生成复杂的核间空间关系。基于上述方法，IM-Diffusion生成具有不同布局和空间关系的病理图像，有效支持下游任务。在CoNSeP和GLySAC数据集上的评估表明，所生成图像能显著提升实例分割整体性能。代码将在后续公开。
原文摘要 · Abstract (English)
Nuclear instance segmentation plays a vital role in disease diagnosis within digital pathology. However, limited labeled data in pathological images restricts the overall performance of nuclear instance segmentation. To tackle this challenge, we propose a novel data augmentation framework Instance Migration Diffusion Model (IM-Diffusion), IM-Diffusion designed to generate more varied pathological images by constructing diverse nuclear layouts and internuclear spatial relationships. In detail, we introduce a Nuclear Migration Module (NMM) which constructs diverse nuclear layouts by simulating the process of nuclear migration. Building on this, we further present an Internuclear-regions Inpainting Module (IIM) to generate diverse internuclear spatial relationships by structure-aware inpainting. On the basis of the above, IM-Diffusion generates more diverse pathological images with different layouts and internuclear spatial relationships, thereby facilitating downstream tasks. Evaluation on the CoNSeP and GLySAC datasets demonstrate that the images generated by IM-Diffusion effectively enhance overall instance segmentation performance. Code will be made public later.
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