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.

翻译：背景与目标：结直肠癌组织病理学分级依赖于腺体结构的精确分割。当前深度学习方法需要大规模像素级标注，这在常规临床实践中既费力又难以获取。弱监督语义分割提供了一种有前景的替代方案。然而，基于类别激活图的方法通常产生不完整的伪掩码，其过度强调高判别性区域而无法监督未标注的腺体结构。我们提出一种弱监督师生框架，利用稀疏病理学家标注和指数移动平均稳定的教师网络来生成优化的伪掩码。方法：该框架集成基于置信度的过滤、教师预测与有限真实标注的自适应融合，以及课程引导的优化策略，以渐进式分割未标注的腺体区域。该方法在俄亥俄州立大学韦克斯纳医学中心的机构结直肠癌队列（包含60张苏木精-伊红染色全切片图像）以及公开数据集上进行了评估，包括Gland Segmentation数据集、TCGA COAD、TCGA READ和SPIDER。结果：在Gland Segmentation数据集上，该框架取得了80.10的平均交并比和89.10的平均Dice系数。跨队列评估显示，在TCGA COAD和TCGA READ数据集上无需额外标注即具有鲁棒的泛化能力，而在SPIDER数据集上的性能下降反映了领域偏移问题。结论：所提框架为结直肠组织病理学中的腺体分割提供了一种标注高效且可泛化的解决方案。