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Neurocomputing v.229, 2017년, pp.77 - 87   SCIE
본 등재정보는 저널의 등재정보를 참고하여 보여주는 베타서비스로 정확한 논문의 등재여부는 등재기관에 확인하시기 바랍니다.

Automatic content understanding with cascaded spatial–temporal deep framework for capsule endoscopy videos

Chen, Honghan (School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China ); Wu, Xiao ( School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China ); Tao, Gan ( Endoscopy Center of West China Hospital, Sichuan University, Chengdu, China ); Peng, Qiang ( School of Information Science and Technology, Southwest Jiaotong University, Chengdu, China );
  • 초록  

    Abstract Capsule endoscopy (CE) is the first-line diagnostic tool for inspecting gastrointestinal (GI) tract diseases. It is a tremendous task on examining and managing the CE videos by endoscopists. Therefore, a computer-aided diagnosis system is desired and urgent. In this paper, a general cascaded spatial–temporal deep framework is proposed to understand the most commonly seen contents of whole GI tract videos. First, the noisy contents such as feces, bile, bubble, and low power images are detected and removed by a Convolutional Neural Network (CNN) model. The clear images are then classified into entrance, stomach, small intestine, and colon by the second CNN. Finally, the topographic segmentation of the whole video is performed with a global temporal integration strategy by Hidden Markov Model (HMM). Compared to existing methods, the proposed framework performs noise content detection and topographic segmentation at the same time, which significantly reduces the number of images to be checked by endoscopists and segments images of different organs more accurately. Experiments on a dataset with 630K images from 14 patients demonstrate that the proposed approach achieves a promising performance in terms of effectiveness and efficiency.


  • 주제어

    Wireless capsule endoscopy .   Convolutional neural network .   Topographic segmentation .   Content understanding .   Hidden Markov model.  

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