通过合成稀有类型细胞核，提升病理图像分割的平衡性与准确性。
NucleiMix: Realistic Data Augmentation for Nuclei Instance Segmentation
- 基于扩散模型分两阶段插入稀有细胞核并融合环境。
- 在三个公开数据集上显著提升罕见细胞核分割性能。
- 适合病理图像分析中数据不平衡场景的研究者使用。
细胞核实例分割是病理图像分析中的关键任务，为诸多下游应用奠定基础。尽管多个公开数据集已推动该领域发展，但现有方法仍面临数据不平衡问题。本文提出一种名为 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各类细胞核分布。NucleiMix 分两阶段进行：第一阶段识别与稀有细胞核周围环境相似的候选位置，并将稀有细胞核插入其中；第二阶段利用预训练扩散模型采用渐进式修复策略，无缝融合稀有细胞核至新环境，替代主类型细胞核或背景区域。我们在三个公开数据集上，使用两种主流细胞核实例分割模型系统评估了 NucleiMix 的有效性。结果表明，NucleiMix 能够高质量合成逼真的稀有类型细胞核，并显著提升细胞核分割与分类的准确性和鲁棒性。
原文摘要 · Abstract (English)
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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