  Capsule endoscopy is an evolutional technique for examining and diagnosing
intractable gastrointestinal diseases. Because of the huge amount of data,
analyzing capsule endoscope videos is very time-consuming and labor-intensive
for gastrointestinal medicalists. The development of intelligent long video
analysis algorithms for regional positioning and analysis of capsule endoscopic
video is therefore essential to reduce the workload of clinicians and assist in
improving the accuracy of disease diagnosis. In this paper, we propose a deep
model to ground shooting range of small intestine from a capsule endoscope
video which has duration of tens of hours. This is the first attempt to attack
the small intestine grounding task using deep neural network method. We model
the task as a 3-way classification problem, in which every video frame is
categorized into esophagus/stomach, small intestine or colorectum. To explore
long-range temporal dependency, a transformer module is built to fuse features
of multiple neighboring frames. Based on the classification model, we devise an
efficient search algorithm to efficiently locate the starting and ending
shooting boundaries of the small intestine. Without searching the small
intestine exhaustively in the full video, our method is implemented via
iteratively separating the video segment along the direction to the target
boundary in the middle. We collect 113 videos from a local hospital to validate
our method. In the 5-fold cross validation, the average IoU between the small
intestine segments located by our method and the ground-truths annotated by
broad-certificated gastroenterologists reaches 0.945.
