Automated Detection and Segmentation of Early Gastric Cancer from Endoscopic Images Using Mask R-CNN

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 region...

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Bibliographic Details
Published in:Applied sciences 2020-06, Vol.10 (11), p.3842
Main Authors: Shibata, Tomoyuki, Teramoto, Atsushi, Yamada, Hyuga, Ohmiya, Naoki, Saito, Kuniaki, Fujita, Hiroshi
Format: Article
Language:English
Subjects:
Analysis
Automation
Cancer
Datasets
Deep learning
Diagnosis
Endoscopy
Gastric cancer
Image detection
Image processing
Image segmentation
Invasiveness
mask R-CNN
Methods
Neural networks
Patients
segmentation
Semantics
Software
Stomach cancer
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app10113842