A station recognition and pancreatic segmentation system in endoscopic ultrasonography based on deep learning
10.3760/cma.j.cn321463-20200325-00245
- VernacularTitle:基于深度学习的超声内镜分站和胰腺分割识别系统
- Author:
Zihua LU
1
;
Huiling WU
;
Liwen YAO
;
Di CHEN
;
Honggang YU
Author Information
1. 武汉大学人民医院消化内科 消化系统疾病湖北省重点实验室 湖北省消化疾病微创诊治医学临床研究中心 430060
- Keywords:
Artificial intelligence;
Quality control;
Pancreas;
Endoscopic ultrasonography;
Deep learning
- From:
Chinese Journal of Digestive Endoscopy
2021;38(10):778-782
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop an endoscopic ultrasonography (EUS) station recognition and pancreatic segmentation system based on deep learning and to validate its efficacy.Methods:Data of 269 EUS procedures were retrospectively collected from Renmin Hospital of Wuhan University between December 2016 and December 2019, and were divided into 3 datasets: (1)Dataset A of 205 procedures for model training containing 16 305 images for classification training and 1 953 images for segmentation training; (2)Dataset B of 44 procedures for model testing containing 1 606 images for classification testing and 480 images for segmentation testing; (3) Dataset C of 20 procedures with 150 images for comparing the performance between models and endoscopists. EUS experts (with more than 10 years of experience) A and B classified and labeled all images of dataset A, B and C through discussion, and the results were used as the gold standard. EUS expert C and senior EUS endoscopists (with more than 5 years of experience) D and E classified and labeled the images in dataset C, and the results were used for comparison with model. The main outcomes included accuracy of classification, Dice (F1 score) of segmentation and Cohen Kappa coefficient of consistency analysis.Results:In test dataset B, the model achieved a mean accuracy of 94.1% in classification. The mean Dice of pancreatic and vascular segmentation were 0.826 and 0.841 respectively. In dataset C, the classification accuracy of the model reached 90.0%. The classification accuracy of expert C, senior endoscopist D and E were 89.3%, 88.7% and 87.3%, respectively. The Dice of pancreatic and vascular segmentation in the model were 0.740 and 0.859, 0.708 and 0.778 for expert C, 0.747 and 0.875 for senior endoscopist D, and 0.774 and 0.789 for senior endoscopist E. The model was comparable to the expert level.Consistency analysis showed that there was high consistency between the model and endoscopists (the Kappa coefficient was 0.823 between model and expert C, 0.840 between model and senior endoscopist D, and 0.799 between model and senior endoscopist E).Conclusion:EUS station classification and pancreatic segmentation system based on deep learning can be used for quality control of pancreatic EUS, with a comparable performance of classification and segmentation to that of EUS experts.
