CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy

Abstract

Background and study aims Capsule endoscopy (CE) is the preferred method for small bowel (SB) exploration. With a mean number of 50,000 SB frames per video, SBCE reading is time-consuming and tedious (30 to 60 minutes per video). We describe a large, multicenter database named CAD-CAP (Computer-Assisted Diagnosis for CAPsule Endoscopy, CAD-CAP). This database aims to serve the development of CAD tools for CE reading. Materials and methods Twelve French endoscopy centers were involved. All available third-generation SB-CE videos (Pillcam, Medtronic) were retrospectively selected from these centers and deidentified. Any pathological frame was extracted and included in the database. Manual segmentation of findings within these frames was performed by two pre-med students trained and supervised by an expert reader. All frames were then classified by type and clinical relevance by a panel of three expert readers. An automated extraction process was also developed to create a dataset of normal, proofread, control images from normal, complete, SB-CE videos. Results Four-thousand-one-hundred-and-seventy-four SB-CE were included. Of them, 1,480 videos (35 %) containing at least one pathological finding were selected. Findings from 5,184 frames (with their short video sequences) were extracted and delimited: 718 frames with fresh blood, 3,097 frames with vascular lesions, and 1,369 frames with inflammatory and ulcerative lesions. Twenty-thousand normal frames were extracted from 206 SB-CE normal videos. CAD-CAP has already been used for development of automated tools for angiectasia detection and also for two international challenges on medical computerized analysis.

Fig. 1

Examples of native small bowel capsule endoscopy frames, with their corresponding delimitations. a Native deidentified still frame showing a vascular lesion considered "highly relevant.” b Manual delimitation of the vascular lesion within frame 1a. c Native deidentified still frame showing a vascular lesion considered " poorly relevant.” d Manual delimitation of the vascular lesion within frame 1c. e Native deidentified still frame showing an ulcerative lesion (considered "highly relevant"). f Manual delimitation of the ulcerative lesion within frame 1e g Manual delimitation of the ulcerative lesion including mucosal inflammatory changes within frame 1e. h Native deidentified still frame showing an ulcero-inflammatory lesion (considered "moderately relevant"). i Manual delimitation of the ulcero-inflammatory lesion within frame 1 hour. j Native deidentified still frame showing fresh blood (considered "highly relevant"). k Manual delimitation of the fresh blood within frame 1j. l , m Two normal control frames (no delimitation needed)

Fig. 2

CAD-CAP database distribution.