Gastrointestinal image stitching based on improved unsupervised algorithm

• Published in: PloS one Vol. 19; no. 9; p. e0310214
• Main Authors: Yan, Rui, Jiang, Yu, Zhang, Chenhao, Tang, Rui, Liu, Ran, Wu, Jinghua, Su, Houcheng
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.09.2024
• Subjects: Accuracy; Algorithms; Analysis; Biology and Life Sciences; Care and treatment; Colonoscopy; Computer and Information Sciences; Computer vision; Datasets; Deep Learning; Diagnosis; Diagnostic imaging; Endoscopes; Endoscopy; Endoscopy, Gastrointestinal - methods; Evaluation; Field of view; Gastrointestinal cancer; Gastrointestinal system; Gastrointestinal tract; Gastrointestinal Tract - diagnostic imaging; Humans; Image enhancement; Image processing; Image Processing, Computer-Assisted - methods; Image reconstruction; Image resolution; Labeling; Machine vision; Medical imaging; Medical imaging equipment; Medical screening; Medicine and Health Sciences; Physical Sciences; Polyps; Research and Analysis Methods; Stitching; Stomach cancer; Supervised learning; Tumors; Unsupervised learning; Unsupervised Machine Learning; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0310214

Image stitching is a traditional but challenging computer vision task. The goal is to stitch together multiple images with overlapping areas into a single, natural-looking, high-resolution image without ghosts or seams. This article aims to increase the field of view of gastroenteroscopy and reduce the missed detection rate. To this end, an improved depth framework based on unsupervised panoramic image stitching of the gastrointestinal tract is proposed. In addition, preprocessing for aberration correction of monocular endoscope images is introduced, and a C2f module is added to the image reconstruction network to improve the network’s ability to extract features. A comprehensive real image data set, GASE-Dataset, is proposed to establish an evaluation benchmark and training learning framework for unsupervised deep gastrointestinal image splicing. Experimental results show that the MSE, RMSE, PSNR, SSIM and RMSE_SW indicators are improved, while the splicing time remains within an acceptable range. Compared with traditional image stitching methods, the performance of this method is enhanced. In addition, improvements are proposed to address the problems of lack of annotated data, insufficient generalization ability and insufficient comprehensive performance in image stitching schemes based on supervised learning. These improvements provide valuable aids in gastrointestinal examination.