Title of article :
Gastroscopic Panoramic View: Application to Automatic Polyps Detection under Gastroscopy
Author/Authors :
Shi, Chenfei Women’s Hospital School of Medicine - ZheJiang University - Hangzhou, China , Xue, Yan Women’s Hospital School of Medicine - ZheJiang University - Hangzhou, China , Jiang, Chuan Women’s Hospital School of Medicine - ZheJiang University - Hangzhou, China , Tian, Hui Women’s Hospital School of Medicine - ZheJiang University - Hangzhou, China , Liu, Bei Women’s Hospital School of Medicine - ZheJiang University - Hangzhou, China
Pages :
8
From page :
1
To page :
8
Abstract :
Endoscopic diagnosis is an important means for gastric polyp detection. In this paper, a panoramic image of gastroscopy is developed, which can display the inner surface of the stomach intuitively and comprehensively. Moreover, the proposed automatic detection solution can help doctors locate the polyps automatically and reduce missed diagnosis. ,e main contributions of this paper are firstly, a gastroscopic panorama reconstruction method is developed. ,e reconstruction does not require additional hardware devices and can solve the problem of texture dislocation and illumination imbalance properly; secondly, an end-to-end multiobject detection for gastroscopic panorama is trained based on a deep learning framework. Compared with traditional solutions, the automatic polyp detection system can locate all polyps in the inner wall of the stomach in real time and assist doctors to find the lesions. ,irdly, the system was evaluated in the Affiliated Hospital of Zhejiang University. ,e results show that the average error of the panorama is less than 2 mm, the accuracy of the polyp detection is 95%, and the recall rate is 99%. In addition, the research roadmap of this paper has guiding significance for endoscopy-assisted detection of other human soft cavities.
Keywords :
Polyps , Gastroscopy , Detection
Journal title :
Computational and Mathematical Methods in Medicine
Serial Year :
2019
Full Text URL :
Record number :
2611489
Link To Document :
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