通过自监督学习和协同训练利用病理图像点标注进行细胞核分割
Nuclei Segmentation with Point Annotations from Pathology Images via Self-Supervised Learning and Co-Training
AI总结：
针对病理图像细胞核分割中像素级标注昂贵的问题，提出仅用点标注的弱监督方法，融合 Voronoi/k-means 粗标签、EMA 协同训练和染色转换自监督表征，在两个公开数据集上达到优于现有方法、接近全监督的性能。
摘要：
细胞核分割是数字病理中全切片图像分析的一项关键任务。通常，全监督学习的分割性能严重依赖标注数据的数量和质量。然而，由专业病理学家提供准确的像素级真值既耗时又昂贵，而获取点标注等粗粒度标签则容易得多。本文提出一种仅需点标注进行训练的弱监督细胞核分割学习方法。首先，为避免过拟合，基于 Voronoi 图和 k-means 聚类方法从点标注生成粗像素级标签。其次，设计一种结合指数滑动平均方法的协同训练策略，以改进粗标签的不完整监督。第三，为病理图像的细胞核分割定制一种自监督视觉表示学习方法，该方法将苏木精成分图像转换为 H&E 染色图像，从而更好地理解细胞核与细胞质之间的关系。我们使用两个公开数据集对所提方法进行全面评估。可视化结果和定量结果均表明，本方法优于现有最先进方法，并且与全监督方法相比具有具有竞争力的性能。代码：https://github.com/hust-linyi/SC-Net
英文摘要：
Nuclei segmentation is a crucial task for whole slide image analysis in digital pathology. Generally, the segmentation performance of fully-supervised learning heavily depends on the amount and quality of the annotated data. However, it is time-consuming and expensive for professional pathologists to provide accurate pixel-level ground truth, while it is much easier to get coarse labels such as point annotations. In this paper, we propose a weakly-supervised learning method for nuclei segmentation that only requires point annotations for training. First, coarse pixel-level labels are derived from the point annotations based on the Voronoi diagram and the k-means clustering method to avoid overfitting. Second, a co-training strategy with an exponential moving average method is designed to refine the incomplete supervision of the coarse labels. Third, a self-supervised visual representation learning method is tailored for nuclei segmentation of pathology images that transforms the hematoxylin component images into the H&E stained images to gain better understanding of the relationship between the nuclei and cytoplasm. We comprehensively evaluate the proposed method using two public datasets. Both visual and quantitative results demonstrate the superiority of our method to the state-of-the-art methods, and its competitive performance compared to the fully-supervised methods. Code: https://github.com/hust-linyi/SC-Net
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。
