A deep learning-based system for accurate detection of anatomical landmarks in colon environment

Ye，Chengwei1; Che，Kaiwei2,3; Yao，Yibing4; Ma，Nachuan5; Zhang，Ruo6; Xu，Yangxin1; Wang，Jiankun6; Meng，Max Q.H.1,6,7

2024-07-01

10.20517/ir.2024.10

Intelligence and Robotics   影响因子和分区

2770-3541

Colonoscopy is a standard imaging tool for examining the lower gastrointestinal tract of patients to capture lesion areas. However, if a lesion area is found during the colonoscopy process, it is difficult to record its location relative to the colon for subsequent therapy or recheck without any reference landmark. Thus, automatic detection of biological anatomical landmarks is highly demanded to improve clinical efficiency. In this article, we propose a novel deep learning-based approach to detect biological anatomical landmarks in colonoscopy videos. First, raw colonoscopy video sequences are pre-processed to reject interference frames. Second, a ResNet-101-based network is used to detect three biological anatomical landmarks separately to obtain the intermediate detection results. Third, to achieve more reliable localization, we propose to post-process the intermediate detection results by identifying the incorrectly predicted frames based on their temporal distribution and reassigning them back to the correct class. Finally, the average detection accuracy reaches 99.75%. Meanwhile, the average intersection over union of 0.91 shows a high degree of similarity between our predicted landmark periods and ground truth. The experimental results demonstrate that our proposed model can accurately detect and localize biological anatomical landmarks from colonoscopy videos.

Biological anatomical landmark detection colonoscopy videos convolutional neural network (CNN) interference frame rejection result cleaning

英语

通讯

2-s2.0-85193021399

Scopus

1.Department of Electronic Engineering,The Chinese University of Hong Kong,999077,Hong Kong
2.School of Electronic and Computer Engineering,Peking University,Beijing,100871,China
3.Peng Cheng Laboratory,Shenzhen,Guangdong,518000,China
4.Department of Oncology,Air Force Medical Center,PLA,Beijing,100142,China
5.College of Electronic & Information Engineering,Tongji University,Shanghai,201804,China
6.Department of Electronic and Electrical Engineering,Southern University of Science and Technology,Shenzhen,Guangdong,518055,China
7.Shenzhen Research Institute of the Chinese,University of Hong Kong,Shenzhen,Guangdong,518057,China

Ye，Chengwei,Che，Kaiwei,Yao，Yibing,et al. A deep learning-based system for accurate detection of anatomical landmarks in colon environment[J]. Intelligence and Robotics,2024,4(2):164-178.

Ye，Chengwei.,Che，Kaiwei.,Yao，Yibing.,Ma，Nachuan.,Zhang，Ruo.,...&Meng，Max Q.H..(2024).A deep learning-based system for accurate detection of anatomical landmarks in colon environment.Intelligence and Robotics,4(2),164-178.

Ye，Chengwei,et al."A deep learning-based system for accurate detection of anatomical landmarks in colon environment".Intelligence and Robotics 4.2(2024):164-178.