A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

Shiliang Ai; Chen Li; Xiaoyan Li; Tao Jiang; Marcin Grzegorzek; Changhao Sun; Md Mamunur Rahaman; Jinghua Zhang; Yudong Yao; Hong Li

doi:10.1155/2021/6671417

A State-of-the-Art Review for Gastric Histopathology Image Analysis Approaches and Future Development

Biomed Res Int. 2021 Jun 26:2021:6671417. doi: 10.1155/2021/6671417. eCollection 2021.

Authors

Shiliang Ai¹, Chen Li¹, Xiaoyan Li², Tao Jiang³, Marcin Grzegorzek⁴, Changhao Sun^{1

4

5}, Md Mamunur Rahaman¹, Jinghua Zhang^{1

4}, Yudong Yao⁶, Hong Li¹

Affiliations

¹ Microscopic Image and Medical Image Analysis Group, College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110169, China.
² Cancer Hospital of China Medical University, Liaoning Cancer Hospital and Institute, Shenyang 110042, China.
³ Control Engineering College, Chengdu University of Information Technology, Chengdu 610103, China.
⁴ Institute of Medical Informatics, University of Luebeck, Luebeck, Germany.
⁵ Shenyang Institute of Automation, Chinese Academy of Sciences, 110169, China.
⁶ Department of Electrical and Computer Engineering, Stevens Institute of Technology, Hoboken, NJ 07030, USA.

Abstract

Gastric cancer is a common and deadly cancer in the world. The gold standard for the detection of gastric cancer is the histological examination by pathologists, where Gastric Histopathological Image Analysis (GHIA) contributes significant diagnostic information. The histopathological images of gastric cancer contain sufficient characterization information, which plays a crucial role in the diagnosis and treatment of gastric cancer. In order to improve the accuracy and objectivity of GHIA, Computer-Aided Diagnosis (CAD) has been widely used in histological image analysis of gastric cancer. In this review, the CAD technique on pathological images of gastric cancer is summarized. Firstly, the paper summarizes the image preprocessing methods, then introduces the methods of feature extraction, and then generalizes the existing segmentation and classification techniques. Finally, these techniques are systematically introduced and analyzed for the convenience of future researchers.

Publication types

Review

MeSH terms

Algorithms
Color
Computer-Aided Design
Diagnosis, Computer-Assisted / methods
Humans
Image Interpretation, Computer-Assisted / methods
Image Processing, Computer-Assisted*
Imaging, Three-Dimensional
Machine Learning
Poisson Distribution
Reproducibility of Results
Stomach / diagnostic imaging*
Stomach / pathology*
Stomach Neoplasms / diagnostic imaging