Evaluation of super resolution technology for digestive endoscopic images
Jiaxi Lin,
Shiqi Zhu,
Xin Gao,
Xiaolin Liu,
Chunfang Xu,
Zhonghua Xu,
Jinzhou Zhu
Affiliations
Jiaxi Lin
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, China; Key Laboratory of Hepatoaplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
Shiqi Zhu
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, China; Key Laboratory of Hepatoaplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
Xin Gao
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, China; Key Laboratory of Hepatoaplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
Xiaolin Liu
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, China; Key Laboratory of Hepatoaplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China
Chunfang Xu
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, China
Zhonghua Xu
Department of Orthopedics, Jintan Affiliated Hospital to Jiangsu University, Changzhou, China; Corresponding author. Department of Orthopedics, Jintan Affiliated Hospital of Jiangsu University, #500 Jintan Avenue, Changzhou, 213200, China.
Jinzhou Zhu
Department of Gastroenterology, The First Affiliated Hospital of Soochow University, Suzhou, China; Suzhou Clinical Center of Digestive Diseases, Suzhou, China; Key Laboratory of Hepatoaplenic Surgery, Ministry of Education, The First Affiliated Hospital of Harbin Medical University, Harbin, China; Corresponding author. Department of Gastroenterology, The First Affiliated Hospital of Soochow University, #188 Shizi St., Suzhou, 215000, China.
Object: This study aims to evaluate the value of super resolution (SR) technology in augmenting the quality of digestive endoscopic images. Methods: In the retrospective study, we employed two advanced SR models, i.e., SwimIR and ESRGAN. Two discrete datasets were utilized, with training conducted using the dataset of the First Affiliated Hospital of Soochow University (12,212 high-resolution images) and evaluation conducted using the HyperKvasir dataset (2,566 low-resolution images). Furthermore, an assessment of the impact of enhanced low-resolution images was conducted using a 5-point Likert scale from the perspectives of endoscopists. Finally, two endoscopic image classification tasks were employed to evaluate the effect of SR technology on computer vision (CV). Results: SwinIR demonstrated superior performance, which achieved a PSNR of 32.60, an SSIM of 0.90, and a VIF of 0.47 in test set. 90 % of endoscopists supported that SR preprocessing moderately ameliorated the readability of endoscopic images. For CV, enhanced images bolstered the performance of convolutional neural networks, whether in the classification task of Barrett's esophagus (improved F1-score: 0.04) or Mayo Endoscopy Score (improved F1-score: 0.04). Conclusions: SR technology demonstrates the capacity to produce high-resolution endoscopic images. The approach enhanced clinical readability and CV models’ performance of low-resolution endoscopic images.
