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

• DOI: 10.1049/el.2020.0729
• Publication Date: 2020-05-02T02:18:41Z
• Authors: Changhao Sun; Chen Li; Jinghua Zhang; Frank Kulwa; Xiaoyan Li
• Keywords: 0202 electrical engineering, electronic engineering, information engineering; 02 engineering and technology; 3. Good health

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 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.