Gastric histopathology image segmentation using a hierarchical
conditional random field
release_fu6uxysxdfd6thauhp35nhkkvm
by
Changhao Sun, Chen Li, Xiaoyan Li
2020
Abstract
In this paper, a Hierarchical Conditional Random Field (HCRF) model based
Gastric Histopathology Image Segmentation (GHIS) method is proposed, which can
localize abnormal (cancer) regions in gastric histopathology images obtained by
optical microscope to assist histopathologists in medical work. First, to
obtain pixel-level segmentation information, we retrain a Convolutional Neural
Network (CNN) to build up our pixel-level potentials. Then, in order to obtain
abundant spatial segmentation information in patch-level, we fine-tune another
three CNNs to build up our patch-level potentials. Thirdly, based on the pixel-
and patch-level potentials, our HCRF model is structured. Finally, graph-based
post-processing is applied to further improve our segmentation performance. In
the experiment, a segmentation accuracy of 78.91% is achieved on a Hematoxylin
and Eosin (H&E) stained gastric histopathological dataset with 560 images,
showing the effectiveness and future potential of the proposed GHIS method.
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