Unsupervised Shot Boundary Detection for Temporal Segmentation of Long Capsule Endoscopy Videos

TL;DR

This paper introduces an unsupervised, efficient method for segmenting long capsule endoscopy videos into meaningful parts, reducing review time for physicians by automatically detecting transition points using deep features and change point detection.

Contribution

The novel approach combines high-level CNN features with a low-dimensional embedding and PELT algorithm for real-time, unsupervised segmentation of lengthy medical videos.

Findings

Achieved 66% AUC in boundary detection on real patient videos.

Reduced computational complexity for clinical application.

Effective segmentation of GI tract videos for medical diagnosis.

Abstract

Physicians use Capsule Endoscopy (CE) as a non-invasive and non-surgical procedure to examine the entire gastrointestinal (GI) tract for diseases and abnormalities. A single CE examination could last between 8 to 11 hours generating up to 80,000 frames which is compiled as a video. Physicians have to review and analyze the entire video to identify abnormalities or diseases before making diagnosis. This review task can be very tedious, time consuming and prone to error. While only as little as a single frame may capture useful content that is relevant to the physicians' final diagnosis, frames covering the small bowel region alone could be as much as 50,000. To minimize physicians' review time and effort, this paper proposes a novel unsupervised and computationally efficient temporal segmentation method to automatically partition long CE videos into a homogeneous and identifiable video segments. However, the search for temporal boundaries in a long video using high dimensional frame-feature matrix is computationally prohibitive and impracticable for real clinical application. Therefore, leveraging both spatial and temporal information in the video, we first extracted high level frame features using a pretrained CNN model and then projected the high-dimensional frame-feature matrix to lower 1-dimensional embedding. Using this 1-dimensional sequence embedding, we applied the Pruned Exact Linear Time (PELT) algorithm to searched for temporal boundaries that indicates the transition points from normal to abnormal frames and vice-versa. We experimented with multiple real patients' CE videos and our model achieved an AUC of 66\% on multiple test videos against expert provided labels.