Image segmentation is a fundamental task in the field of imaging and vision. Supervised deep learning for segmentation has achieved unparalleled success when sufficient training data with annotated labels are available. However, annotation is known to be expensive to obtain, especially for histopathology images where the target regions are usually with high morphology variations and irregular shapes. Thus, weakly supervised learning with sparse annotations of points is promising to reduce the annotation workload. In this work, we propose a contrast-based variational model to generate segmentation results, which serve as reliable complementary supervision to train a deep segmentation model for histopathology images. The proposed method considers the common characteristics of target regions in histopathology images and can be trained in an end-to-end manner. It can generate more regionally consistent and smoother boundary segmentation, and is more robust to unlabeled `novel' regions. Experiments on two different histology datasets demonstrate its effectiveness and efficiency in comparison to previous models.

翻译：图像分割是成像与视觉领域的基础任务。当具备充足标注训练数据时，基于监督学习的深度分割方法取得了空前成功。然而，标注成本高昂，尤其在目标区域形态多变、形状不规则的组织病理图像中更为突出。因此，采用稀疏点标注的弱监督学习有望降低标注工作量。本文提出一种基于对比变分模型的分割结果生成方法，该方法可作为可靠的互补监督信号，用于训练组织病理图像的深度分割模型。所提方法充分考虑了组织病理图像目标区域的共性特征，支持端到端训练，能够生成区域一致性更强、边界更平滑的分割结果，同时对未标注的"新颖"区域具有更强的鲁棒性。在两个不同组织病理数据集上的实验表明，该方法相较现有模型具有显著的有效性与效率优势。