用于病理学中细胞核实例分割的实例迁移扩散
Instance Migration Diffusion for Nuclear Instance Segmentation in Pathology
AI总结：
针对病理图像标注数据有限的问题，提出数据增强框架 IM-Diffusion，通过模拟细胞核迁移和结构感知修复生成多样化病理图像，有效提升了细胞核实例分割性能。
摘要：
细胞核实例分割在数字病理学的疾病诊断中发挥着至关重要的作用。然而，病理图像中标注数据有限，限制了细胞核实例分割的整体性能。为了应对这一挑战，我们提出了一种新颖的数据增强框架 Instance Migration Diffusion Model (IM-Diffusion)，IM-Diffusion 旨在通过构建多样的细胞核布局和核间空间关系来生成更多样化的病理图像。具体而言，我们引入了 Nuclear Migration Module (NMM)，该模块通过模拟细胞核迁移的过程来构建多样的细胞核布局。在此基础上，我们进一步提出了 Internuclear-regions Inpainting Module (IIM)，通过结构感知修复来生成多样的核间空间关系。基于上述内容，IM-Diffusion 生成了具有不同布局和核间空间关系的更多样化病理图像，从而促进下游任务。在 CoNSeP 和 GLySAC 数据集上的评估表明，IM-Diffusion 生成的图像有效提升了整体实例分割性能。代码将在稍后公开。
英文摘要：
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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