# Gastric histopathology image segmentation using a hierarchical conditional random field

arXiv: https://arxiv.org/abs/2003.01302 · HTML: https://mlsanity.com/paper/2003.01302/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Mar 2020.

## Built on by (most cited first, 16 of 16)

- [A Comprehensive Review of Computer-aided Whole-slide Image Analysis: from Datasets to Feature Extraction, Segmentation, Classification, and Detection Approaches](https://mlsanity.com/paper/2102.10553/) (2021)
- [GasHisSDB: A New Gastric Histopathology Image Dataset for Computer Aided Diagnosis of Gastric Cancer](https://mlsanity.com/paper/2106.02473/) (2021)
- [EBHI-Seg: A Novel Enteroscope Biopsy Histopathological Haematoxylin and Eosin Image Dataset for Image Segmentation Tasks](https://mlsanity.com/paper/2212.00532/) (2022)
- [Applications of Artificial Neural Networks in Microorganism Image Analysis: A Comprehensive Review from Conventional Multilayer Perceptron to Popular Convolutional Neural Network and Potential Visual Transformer](https://mlsanity.com/paper/2108.00358/) (2021)
- [A State-of-the-art Survey of Object Detection Techniques in Microorganism Image Analysis: From Classical Methods to Deep Learning Approaches](https://mlsanity.com/paper/2105.03148/) (2021)
- [A Hierarchical Conditional Random Field-based Attention Mechanism Approach for Gastric Histopathology Image Classification](https://mlsanity.com/paper/2102.10499/) (2021)
- [A State-of-the-art Survey of Artificial Neural Networks for Whole-slide Image Analysis:from Popular Convolutional Neural Networks to Potential Visual Transformers](https://mlsanity.com/paper/2104.06243/) (2021)
- [Application of Transfer Learning and Ensemble Learning in Image-level Classification for Breast Histopathology](https://mlsanity.com/paper/2204.08311/) (2022)
- [A Survey of Semen Quality Evaluation in Microscopic Videos Using Computer Assisted Sperm Analysis](https://mlsanity.com/paper/2202.07820/) (2022)
- [EBHI:A New Enteroscope Biopsy Histopathological H&E Image Dataset for Image Classification Evaluation](https://mlsanity.com/paper/2202.08552/) (2022)
- [EMDS-7: Environmental Microorganism Image Dataset Seventh Version for Multiple Object Detection Evaluation](https://mlsanity.com/paper/2110.07723/) (2021)
- [TOD-CNN: An Effective Convolutional Neural Network for Tiny Object Detection in Sperm Videos](https://mlsanity.com/paper/2204.08166/) (2022)
- [A Comparative Study of Gastric Histopathology Sub-size Image Classification: from Linear Regression to Visual Transformer](https://mlsanity.com/paper/2205.12843/) (2022)
- [Unleashing Foundation Vision Models: Adaptive Transfer for Diverse Data-Limited Scientific Domains](https://mlsanity.com/paper/2512.22664/) (2025)
- [ECPC-IDS:A benchmark endometrail cancer PET/CT image dataset for evaluation of semantic segmentation and detection of hypermetabolic regions](https://mlsanity.com/paper/2308.08313/) (2023)
- [Application of Graph Based Features in Computer Aided Diagnosis for Histopathological Image Classification of Gastric Cancer](https://mlsanity.com/paper/2205.08467/) (2022)

## Abstract (arXiv)

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.