Abstract
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.
Abstract (translated)
核实例分割在数字病理学中的疾病诊断中扮演着至关重要的角色。然而，由于病理图像中的标注数据有限，这限制了核实例分割的整体性能。为了解决这一挑战，我们提出了一种新颖的数据增强框架——实例迁移扩散模型（IM-Diffusion）。IM-Diffusion 旨在通过构建多样化的核布局和细胞间空间关系来生成更多样化的病理图像。 具体来说，我们引入了一个名为核迁移模块（NMM）的组件，该模块通过模拟核迁移的过程来构造多样的核布局。在此基础上，进一步提出了一种细胞间区域修复模块（IIM），用于通过结构感知的修补技术生成多样化的细胞间空间关系。基于上述设计，IM-Diffusion 能够生成具有不同布局和细胞间空间关系的多样化病理图像，从而为下游任务提供支持。 在 CoNSeP 和 GLySAC 数据集上的评估表明，由 IM-Diffusion 生成的图像有效提升了整体实例分割性能。代码将在后续公开发布。
URL
https://arxiv.org/abs/2504.01577
