Abstract
The wireless capsule endoscopy has revolutionized early diagnosis of small bowel diseases. However, a single examination has up to 10 h of video and requires between 30–120 min to read. Computational methods are needed to increase both efficiency and accuracy of the diagnosis. In this paper, an evaluation of deep learning U-Net architecture is presented, to detect and segment red lesions in the small bowel. Its results were compared with those obtained from the literature review. To make the evaluation closer to those used in clinical environments, the U-Net was also evaluated in an annotated sequence by using the Suspected Blood Indicator tool (SBI). Results found that detection and segmentation using U-Net outperformed both the algorithms used in the literature review and the SBI tool.
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Notes
- 1.
At the time of submission these datasets were waiting for publication approval from the Ethical Council. In case of approval it will be available at https://rdm.inesctec.pt/dataset/nis-2018-003.
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Acknowledgments
This work is financed by the ERDF – European Regional Development Fund through the Operational Program for Competitiveness and Internationalization - COMPETE 2020 Program within project 
POCI-01-0145-FEDER-006961
, and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia as part of project 
UID/EEA/50014/2013
.
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Coelho, P., Pereira, A., Leite, A., Salgado, M., Cunha, A. (2018). A Deep Learning Approach for Red Lesions Detection in Video Capsule Endoscopies. In: Campilho, A., Karray, F., ter Haar Romeny, B. (eds) Image Analysis and Recognition. ICIAR 2018. Lecture Notes in Computer Science(), vol 10882. Springer, Cham. https://doi.org/10.1007/978-3-319-93000-8_63
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