Hierarchical conditional random field model for multi‐object segmentation in gastric histopathology images

Abstract

In this Letter, a hierarchical conditional random field (HCRF) model-based gastric histopathology image segmentation (GHIS) method is proposed, which can localise abnormal (cancer) regions in gastric histopathology images to assist histopathologists in medical work. First, to obtain pixel-level segmentation information, the authors retrain a convolutional neural network (CNN) to build up their pixel-level potentials. Then, to obtain abundant spatial segmentation information in patch level, they fine tune another three CNNs to build up their patch-level potentials. Thirdly, based on the pixel- and patch-level potentials, their HCRF model is structured. Finally, a graph-based post-processing is applied to further improve their segmentation performance. In the experiment, a segmentation accuracy of 78.91 % is achieved on a haematoxylin and eosin stained gastric histopathological dataset with 560 images, showing the effectiveness and future potential of the proposed GHIS method.