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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 seg-mentation 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.
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https://hal.archives-ouvertes.fr/hal-02492408
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Submitted on : Wednesday, February 26, 2020 - 10:41:22 PM
Last modification on : Friday, August 5, 2022 - 2:46:00 PM
Long-term archiving on: : Wednesday, May 27, 2020 - 8:47:50 PM

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Romain Leenhardt, Cynthia Li, Jean-Philippe Le Mouel, Gabriel Rahmi, Jean Christophe Saurin, et al.. CAD-CAP: a 25,000-image database serving the development of artificial intelligence for capsule endoscopy. Endoscopy International Open, 2020, 8 (3), pp.E415-E420. ⟨10.1055/a-1035-9088⟩. ⟨hal-02492408⟩

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