Automated detection and segmentation of early gastric cancer from endoscopic images using mask R-CNN

Research output: Contribution to journalArticle

Abstract

Gastrointestinal endoscopy is widely conducted for the early detection of gastric cancer. However, it is often difficult to detect early gastric cancer lesions and accurately evaluate the invasive regions. Our study aimed to develop a detection and segmentation method for early gastric cancer regions from gastrointestinal endoscopic images. In this method, we first collected 1208 healthy and 533 cancer images. The gastric cancer region was detected and segmented from endoscopic images using Mask R-CNN, an instance segmentation method. An endoscopic image was provided to the Mask R-CNN, and a bounding box and a label image of the gastric cancer region were obtained. As a performance evaluation via five-fold cross-validation, sensitivity and false positives (FPs) per image were 96.0% and 0.10 FP/image, respectively. In the evaluation of segmentation of the gastric cancer region, the average Dice index was 71%. These results indicate that our proposed scheme may be useful for the detection of gastric cancer and evaluation of the invasive region in gastrointestinal endoscopy.

Original languageEnglish
Article number3842
JournalApplied Sciences (Switzerland)
Volume10
Issue number11
DOIs
Publication statusPublished - 01-06-2020

All Science Journal Classification (ASJC) codes

  • Materials Science(all)
  • Instrumentation
  • Engineering(all)
  • Process Chemistry and Technology
  • Computer Science Applications
  • Fluid Flow and Transfer Processes

Fingerprint Dive into the research topics of 'Automated detection and segmentation of early gastric cancer from endoscopic images using mask R-CNN'. Together they form a unique fingerprint.

  • Cite this