Polyp Detection and Segmentation from Video Capsule Endoscopy: A Review
This is an incremental review that addresses the problem of automating polyp detection for medical professionals using VCE, which is crucial due to the large-scale video data involved.
The paper reviews existing approaches for automatically detecting and segmenting polyps in video capsule endoscopy (VCE) imagery, analyzing the challenges posed by VCE's unique imaging characteristics compared to traditional endoscopy.
Video capsule endoscopy (VCE) is used widely nowadays for visualizing the gastrointestinal (GI) tract. Capsule endoscopy exams are prescribed usually as an additional monitoring mechanism and can help in identifying polyps, bleeding, etc. To analyze the large scale video data produced by VCE exams automatic image processing, computer vision, and learning algorithms are required. Recently, automatic polyp detection algorithms have been proposed with various degrees of success. Though polyp detection in colonoscopy and other traditional endoscopy procedure based images is becoming a mature field, due to its unique imaging characteristics detecting polyps automatically in VCE is a hard problem. We review different polyp detection approaches for VCE imagery and provide systematic analysis with challenges faced by standard image processing and computer vision methods.