Pseudo Training Data Generation for Unsupervised Cell Membrane Segmentation in Immunohistochemistry Images

In the realm of clinical diagnostics and medical research, quantitative assessment of membrane activity in immunohistochemistry (IHC) images is standard practice. Despite a high demand for cell membrane segmentation, only a few algorithms have been developed, and there is a lack of open datasets in this field. In this paper, we propose a three-stage unsupervised framework to accurately segment positive cell membranes in IHC images. Our approach transforms the unsupervised segmentation task into a supervised one by generating pseudo-paired training data using Voronoi diagrams and CycleGAN. Additionally, we introduce a dual encoder segmentation model with domain adaptation modules to mitigate the domain shift between generated images and real images. To our best knowledge, this is the first work focusing on unsupervised learning for IHC cell membrane segmentation. Extensive experiments and ablation studies on our newly built IHC cell membrane segmentation dataset validate the effectiveness of our framework.