Gastric histopathology image segmentation using a hierarchical conditional random field
This is an incremental improvement for medical image analysis in gastric cancer diagnosis, assisting histopathologists.
The paper tackles gastric cancer diagnosis by proposing a Hierarchical Conditional Random Field (HCRF) method to segment abnormal regions in histopathology images, achieving segmentation accuracy, recall, and specificity of 78.91%, 65.59%, and 81.33% on a test set.
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.