# Gastric histopathology image segmentation using a hierarchical conditional random field Source: [Gastric histopathology image segmentation using a hierarchical conditional random field]() Collection index: [llms.txt]() Data updated: 2026-01-01T13:07:50.960Z Authors: Changhao Sun, Chen Li, Jinghua Zhang, Muhammad Rahaman, Shiliang Ai, Hao Chen, Frank Kulwa, Yixin Li, Xiaoyan Li, Tao Jiang Published: 2020-03-03T02:44:31.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. - [Image Segmentation on GHIS](): Image Segmentation; GHIS; Dice: 46.29. - [Image Segmentation on Gastric Histopathology Image Dataset Tissue-scale](): Image Segmentation; Gastric Histopathology Image Dataset Tissue-scale; Accuracy: 78.91. - [Image Segmentation on Gastric Histopathology Image Dataset Cell-scale](): Image Segmentation; Gastric Histopathology Image Dataset Cell-scale; Time per slice (s): not recorded. ## Original sources and implementation [Original paper]() [PDF]() [Implementation]()