基于扩散的细胞核图像分割数据增强方法
Diffusion-based Data Augmentation for Nuclei Image Segmentation
AI总结：
本文提出首个面向细胞核分割的扩散数据增强方法，通过无条件与条件扩散模型两步生成带实例标注的病理图像，仅用10%真实标注即可达到全监督基线性能。
摘要：
细胞核分割是组织病理学图像定量分析中一项基础但具有挑战性的任务。尽管基于全监督深度学习的方法已取得显著进展，但要获得优异的分割性能仍需要大量带标注图像。考虑到为数据集中的所有细胞核实例进行人工标注效率低下，获取大规模人工标注数据集既耗时又费力。因此，对仅有少量标注图像的数据集进行增强以提升分割性能，具有重要的研究与应用价值。本文提出首个用于细胞核分割的基于扩散的增强方法。其思路是合成大量带标注图像，以促进分割模型的训练。为此，我们提出一种两步策略：第一步，训练一个无条件扩散模型来合成“细胞核结构”，该结构被定义为像素级语义与距离变换的表示。每个合成的细胞核结构都将作为组织病理学图像合成的约束，并进一步经过后处理生成实例图。第二步，训练一个条件扩散模型，根据细胞核结构合成组织病理学图像。合成的组织病理学图像与合成实例图配对后，将被加入真实数据集，用于训练分割模型。实验结果表明，通过用合成样本增强仅含10%标注数据的真实数据集，可以达到与全监督基线相当的分割效果。代码已发布于：https://github.com/lhaof/Nudiff
英文摘要：
Nuclei segmentation is a fundamental but challenging task in the quantitative analysis of histopathology images. Although fully-supervised deep learning-based methods have made significant progress, a large number of labeled images are required to achieve great segmentation performance. Considering that manually labeling all nuclei instances for a dataset is inefficient, obtaining a large-scale human-annotated dataset is time-consuming and labor-intensive. Therefore, augmenting a dataset with only a few labeled images to improve the segmentation performance is of significant research and application value. In this paper, we introduce the first diffusion-based augmentation method for nuclei segmentation. The idea is to synthesize a large number of labeled images to facilitate training the segmentation model. To achieve this, we propose a two-step strategy. In the first step, we train an unconditional diffusion model to synthesize the Nuclei Structure that is defined as the representation of pixel-level semantic and distance transform. Each synthetic nuclei structure will serve as a constraint on histopathology image synthesis and is further post-processed to be an instance map. In the second step, we train a conditioned diffusion model to synthesize histopathology images based on nuclei structures. The synthetic histopathology images paired with synthetic instance maps will be added to the real dataset for training the segmentation model. The experimental results show that by augmenting 10% labeled real dataset with synthetic samples, one can achieve comparable segmentation results with the fully-supervised baseline. The code is released in: https://github.com/lhaof/Nudiff
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
