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Fully-Automated Semantic Segmentation of Wireless Capsule Endoscopy Abnormalities
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Fully-Automated Semantic Segmentation of Wireless Capsule Endoscopy Abnormalities
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.
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.
