  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.
