  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.
