Abstract
Background and objectives: Colorectal cancer histopathological grading depends on accurate segmentation of glandular structures. Current deep learning approaches rely on large scale pixel level annotations that are labor intensive and difficult to obtain in routine clinical practice. Weakly supervised semantic segmentation offers a promising alternative. However, class activation map based methods often produce incomplete pseudo masks that emphasize highly discriminative regions and fail to supervise unannotated glandular structures. We propose a weakly supervised teacher student framework that leverages sparse pathologist annotations and an Exponential Moving Average stabilized teacher network to generate refined pseudo masks. Methods: The framework integrates confidence based filtering, adaptive fusion of teacher predictions with limited ground truth, and curriculum guided refinement to progressively segment unannotated glandular regions. The method was evaluated on an institutional colorectal cancer cohort from The Ohio State University Wexner Medical Center consisting of 60 hematoxylin and eosin stained whole slide images and on public datasets including the Gland Segmentation dataset, TCGA COAD, TCGA READ, and SPIDER. Results: On the Gland Segmentation dataset the framework achieved a mean Intersection over Union of 80.10 and a mean Dice coefficient of 89.10. Cross cohort evaluation demonstrated robust generalization on TCGA COAD and TCGA READ without additional annotations, while reduced performance on SPIDER reflected domain shift. Conclusions: The proposed framework provides an annotation efficient and generalizable approach for gland segmentation in colorectal histopathology.
Abstract (translated)
背景与目标：结直肠癌的组织病理学分级依赖于腺体结构的精确分割。当前深度学习方法依赖于大规模像素级别的注释，这些注释在常规临床实践中耗时且难以获取。弱监督语义分割提供了一种有前景的替代方案。然而，基于类激活图的方法往往会产生不完整的伪掩模，过分强调具有高度区分性的区域，并且无法对未标注的腺体结构进行监督。我们提出了一种利用稀疏病理学家注释和指数移动平均稳定的教师网络生成精炼伪掩模的弱监督师生框架。 方法：该框架结合了基于置信度过滤、教师预测与有限真实标签自适应融合以及课程引导细化，逐步分割未标注的腺体区域。研究在来自俄亥俄州立大学韦克斯纳医学中心的一个机构结直肠癌队列上进行了评估，包括60张苏木精和伊红染色的全切片图像，并且还在公开数据集（如Gland Segmentation 数据集、TCGA COAD、TCGA READ 和 SPIDER）上进行了测试。 结果：在Gland Segmentation 数据集中，该框架实现了平均交并比为80.10%和平均Dice系数为89.10%。跨队列评估显示，在没有额外注释的情况下对TCGA COAD 和 TCGA READ 的泛化能力稳健，而SPIDER上的性能下降反映了域偏移。 结论：所提出的框架提供了一种注解效率高且具有广泛适用性的方法，用于结直肠组织病理学中的腺体分割。
URL
https://arxiv.org/abs/2603.08605
