Fully-Automated Semantic Segmentation of Wireless Capsule Endoscopy Abnormalities

• Published in: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI) pp. 221 - 224
• Main Authors: Paul, Sukriti, Gundabattula, Hanitha Devi, Seelamantula, Chandra Sekhar, Mujeeb, V.R., Prasad, A.S.
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01-04-2020
• Subjects: Gastrointestinal tract; Semantic segmentation; U-Net; Wireless capsule endoscopy
• ISSN: 1945-8452
• DOI: 10.1109/ISBI45749.2020.9098634

Wireless capsule endoscopy (WCE) is a minimally invasive procedure performed with a tiny swallowable optical endoscope that allows exploration of the human digestive tract. The medical device transmits tens of thousands of colour images, which are manually reviewed by a medical expert. This paper highlights the significance of using inputs from multiple colour spaces to train a classical U-Net model for automated semantic segmentation of eight WCE abnormalities. We also present a novel approach of grouping similar abnormalities during the training phase. Experimental results on the KID datasets demonstrate that a U-Net with 4-channel inputs outperforms the single-channel U-Net providing state-of-the-art semantic segmentation of WCE abnormalities.