  For the Convolutional Neural Networks (CNNs) applied in the intelligent
diagnosis of gastric cancer, existing methods mostly focus on individual
characteristics or network frameworks without a policy to depict the integral
information. Mainly, Conditional Random Field (CRF), an efficient and stable
algorithm for analyzing images containing complicated contents, can
characterize spatial relation in images. In this paper, a novel Hierarchical
Conditional Random Field (HCRF) based Gastric Histopathology Image Segmentation
(GHIS) method is proposed, which can automatically localize abnormal (cancer)
regions in gastric histopathology images obtained by an optical microscope to
assist histopathologists in medical work. This HCRF model is built up with
higher order potentials, including pixel-level and patch-level potentials, and
graph-based post-processing is applied to further improve its segmentation
performance. Especially, a CNN is trained to build up the pixel-level
potentials and another three CNNs are fine-tuned to build up the patch-level
potentials for sufficient spatial segmentation information. In the experiment,
a hematoxylin and eosin (H&E) stained gastric histopathological dataset with
560 abnormal images are divided into training, validation and test sets with a
ratio of 1 : 1 : 2. Finally, segmentation accuracy, recall and specificity of
78.91%, 65.59%, and 81.33% are achieved on the test set. Our HCRF model
demonstrates high segmentation performance and shows its effectiveness and
future potential in the GHIS field.
