A study on automated segmentation of blood regions in Wireless Capsule Endoscopy images using fully convolutional networks

Wireless Capsule Endoscopy (WCE) is a novel diagnostic modality of endoscopic imaging which facilitates direct visualization of the gastrointestinal (GI) tract. Many computational methods that can automatically detect and/or characterize the abnormalities from WCE sequences are developed to support medical decision-making. This paper presents a new approach for automated segmentation of blood regions in WCE images via a deep learning strategy. The proposed method first classify the bleeding samples into active and inactive subgroups based on the statistical features derived from the histogram probability of the color space. Then for each subgroup, we highlight the blood regions via fully convolutional networks (FCNs). Experimental results on the clinical WCE dataset demonstrate the efficacy of our approach, which achieves comparable or better performance than the state-of-the-art methods.