Deep Learning-based Biological Anatomical Landmark Detection in Colonoscopy Videos

• URL: http://arxiv.org/abs/2108.02948v1
• Date: Fri, 6 Aug 2021 05:52:32 GMT
• Title: Deep Learning-based Biological Anatomical Landmark Detection in Colonoscopy Videos
• Authors: Kaiwei Che, Chengwei Ye, Yibing Yao, Nachuan Ma, Ruo Zhang, Jiankun Wang, and Max Q.-H. Meng
• Abstract summary: We propose a novel deep learning-based approach to detect biological anatomical landmarks in colonoscopy videos. Average detection accuracy reaches 99.75%, while the average IoU of 0.91 shows a high degree of similarity between our predicted landmark periods and ground truth.
• Score: 21.384094148149003
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Colonoscopy is a standard imaging tool for visualizing the entire gastrointestinal (GI) tract of patients to capture lesion areas. However, it takes the clinicians excessive time to review a large number of images extracted from colonoscopy videos. Thus, automatic detection of biological anatomical landmarks within the colon is highly demanded, which can help reduce the burden of clinicians by providing guidance information for the locations of lesion areas. 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 of the landmark periods within the whole video period, 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 IoU 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 is capable of accurately detecting and localizing biological anatomical landmarks from colonoscopy videos.

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