Real-Time Multi-Label Upper Gastrointestinal Anatomy Recognition from Gastroscope Videos

作者：Tao Yu, Huiyi Hu, Xinsen Zhang, Honglin Lei, Jiquan Liu, Weiling Hu, Huilong Duan, Jianmin Si · 发表于：Applied Sciences · 年份：2022 · DOI：10.3390/app12073306 · 被引用次数：4 · 研究领域：Colorectal Cancer Screening and Detection、Gastric Cancer Management and Outcomes、Esophageal Cancer Research and Treatment

Esophagogastroduodenoscopy (EGD) is a critical step in the diagnosis of upper gastrointestinal disorders. However, due to inexperience or high workload, there is a wide variation in EGD performance by endoscopists. Variations in performance may result in exams that do not completely cover all anatomical locations of the stomach, leading to a potential risk of missed diagnosis of gastric diseases. Numerous guidelines or expert consensus have been proposed to assess and optimize the quality of endoscopy. However, there is a lack of mature and robust methods to accurately apply to real clinical real-time video environments. In this paper, we innovatively define the problem of recognizing anatomical locations in videos as a multi-label recognition task. This can be more consistent with the model learning of image-to-label mapping relationships. We propose a combined structure of a deep learning model (GL-Net) that combines a graph convolutional network (GCN) with long short-term memory (LSTM) networks to both extract label features and correlate temporal dependencies for accurate real-time anatomical locations identification in gastroscopy videos. Our methodological evaluation dataset is based on complete videos of real clinical examinations. A total of 29,269 images from 49 videos were collected as a dataset for model training and validation. Another 1736 clinical videos were retrospectively analyzed and evaluated for the application of the proposed model. Our method achieves 97...