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.

本研究提出了一种交互式分割方法，通过点击交互，利用基于点击的注意力模块（CGAM）将用户提供的点击约束和特征图中的语义信息相结合，并将其公式化为优化问题，相比现有方法，该方法可避免分割结果过度改变导致用户点击的过拟合问题，模型大小与输入图像大小无关，实验结果表明，该方法优于现有的最先进方法。