Fully-Automated Semantic Segmentation of Wireless Capsule Endoscopy Abnormalities

Paul, S and Gundabattula, HD and Seelamantula, CS and Mujeeb, VR and Prasad, AS (2020) Fully-Automated Semantic Segmentation of Wireless Capsule Endoscopy Abnormalities. In: 17th IEEE International Symposium on Biomedical Imaging, ISBI 2020, 3 - 7 Apr 2020, Iowa City, pp. 221-224.

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Abstract

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. © 2020 IEEE.

Item Type:	Conference Paper
Publication:	Proceedings - International Symposium on Biomedical Imaging
Publisher:	IEEE Computer Society
Additional Information:	The copyright for this article belongs to IEEE Computer Society
Keywords:	Medical imaging; Semantics, Fully automated; Human digestive tract; Medical experts; Minimally invasive; Optical endoscope; Semantic segmentation; State of the art; Wireless capsule endoscopy, Endoscopy
Department/Centre:	Division of Electrical Sciences > Electrical Engineering
Date Deposited:	05 Nov 2021 09:19
Last Modified:	05 Nov 2021 09:19
URI:	http://eprints.iisc.ac.in/id/eprint/65866

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