Open AccessHierarchical conditional random field model for multi‐object segmentation in gastric histopathology images
Author(s) -
Sun Changhao,
Li Chen,
Zhang Jinghua,
Kulwa Frank,
Li Xiaoyan
Publication year - 2020
Publication title -
electronics letters
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.375
H-Index - 146
eISSN - 1350-911X
pISSN - 0013-5194
DOI - 10.1049/el.2020.0729
Subject(s) - conditional random field , segmentation , artificial intelligence , computer science , pattern recognition (psychology) , image segmentation , pixel , histopathology , computer vision , scale space segmentation , convolutional neural network , medicine , pathology
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.