NucleiMix：用于细胞核实例分割的真实数据增强方法
NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation
AI总结：
本文提出 NucleiMix 数据增强方法，通过识别相似位置并利用扩散模型渐进式修复插入稀有类型细胞核，有效缓解数据不平衡，提升细胞核实例分割与分类性能。
摘要：
细胞核实例分割是病理图像分析中的一项基础任务，为许多下游应用奠定基础。多个公开数据集的发布显著推动了该领域的研究，但许多现有方法仍面临数据不平衡问题的困扰。为应对这一挑战，本研究提出了一种名为 NucleiMix 的数据增强方法，旨在通过增加数据集中稀有类型细胞核的数量来平衡细胞核类型的分布。NucleiMix 分为两个阶段运行。在第一阶段，它识别与稀有类型细胞核周围环境相似的候选位置，并将稀有类型细胞核插入这些候选位置。在第二阶段，它采用基于预训练扩散模型的渐进式图像修复策略，将稀有类型细胞核无缝整合到新环境中，以替代主要类型细胞核或背景位置。我们在三个公开数据集上使用两种流行的细胞核实例分割模型，系统评估了 NucleiMix 的有效性。结果表明，NucleiMix 在合成逼真的稀有类型细胞核以及以准确且稳健的方式提升细胞核分割和分类质量方面具有优越能力。
英文摘要：
Nuclei instance segmentation is an essential task in pathology image analysis, serving as the foundation for many downstream applications. The release of several public datasets has significantly advanced research in this area, yet many existing methods struggle with data imbalance issues. To address this challenge, this study introduces a data augmentation method, called NucleiMix, which is designed to balance the distribution of nuclei types by increasing the number of rare-type nuclei within datasets. NucleiMix operates in two phases. In the first phase, it identifies candidate locations similar to the surroundings of rare-type nuclei and inserts rare-type nuclei into the candidate locations. In the second phase, it employs a progressive inpainting strategy using a pre-trained diffusion model to seamlessly integrate rare-type nuclei into their new environments in replacement of major-type nuclei or background locations. We systematically evaluate the effectiveness of NucleiMix on three public datasets using two popular nuclei instance segmentation models. The results demonstrate the superior ability of NucleiMix to synthesize realistic rare-type nuclei and to enhance the quality of nuclei segmentation and classification in an accurate and robust manner.
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