Tumor region segmentation is an essential task for the quantitative analysis of digital pathology. Recently presented deep neural networks have shown state-of-the-art performance in various image-segmentation tasks. However, because of the unclear boundary between the cancerous and normal regions in pathology images, despite using modern methods, it is difficult to produce satisfactory segmentation results in terms of the reliability and accuracy required for medical data. In this study, we propose an interactive segmentation method that allows users to refine the output of deep neural networks through click-type user interactions. The primary method is to formulate interactive segmentation as an optimization problem that leverages both user-provided click constraints and semantic information in a feature map using a click-guided attention module (CGAM). Unlike other existing methods, CGAM avoids excessive changes in segmentation results, which can lead to the overfitting of user clicks. Another advantage of CGAM is that the model size is independent of input image size. Experimental results on pathology image datasets indicated that our method performs better than existing state-of-the-art methods.

翻译：肿瘤区域分割是数字病理学定量分析中的关键任务。近期提出的深度神经网络已在多种图像分割任务中展现出最先进性能。然而，由于病理图像中癌变区域与正常组织区域边界模糊，即使采用现代方法，也难以在医学数据所需的可靠性和准确性方面获得令人满意的分割结果。本研究提出一种交互式分割方法，允许用户通过点击式交互对深度神经网络输出进行修正。核心方法是将交互式分割建模为优化问题，通过点击引导注意力模块（CGAM）同时利用用户提供的点击约束与特征图中的语义信息。与现有方法不同，CGAM能避免分割结果的过度变化，从而防止用户点击导致的过拟合现象。该方法另一优势在于模型规模与输入图像尺寸无关。在病理图像数据集上的实验结果表明，本方法性能优于现有最先进方法。