An unsupervised method for histological image segmentation based on tissue cluster level graph cut

While deep learning models have demonstrated outstanding performance in medical image segmentation tasks, histological annotations for training deep learning models are usually challenging to obtain, due to the effort and experience required to carefully delineate tissue structures. In this study, we propose an unsupervised method, termed as tissue cluster level graph cut (TisCut), for segmenting histological images into meaningful compartments (e.g., tumor or non-tumor regions), which aims at assisting histological annotations for downstream supervised models. The TisCut consists of three modules. First, histological tissue objects are clustered based on their spatial proximity and morphological features. The Voronoi diagram is then constructed based on tissue object clustering. In the last module, morphological features computed from the Voronoi diagram are integrated into a region adjacency graph. Image partition is then performed to divide the image into meaningful compartments by using the graph cut algorithm. The TisCut has been evaluated on three histological image sets for necrosis and melanoma detections. Experiments show that the TisCut could provide a comparative performance with U-Net models, which achieves about 70% and 85% Jaccard index coefficients in partitioning brain and skin histological images, respectively. In addition, it shows the potential to be used for generating histological annotations when training masks are difficult to collect for supervised segmentation models.

Automated summary

This study introduces a new unsupervised method, TisCut, for assisting tissue image segmentation and annotations, showing comparative performance with U-Net models in necrosis and melanoma detections.