Application of Artificial Intelligence in Capsule Endoscopy: Where Are We Now?

Unlike wired endoscopy, capsule endoscopy requires additional time for a clinical specialist to review the operation and examine the lesions. To reduce the tedious review time and increase the accuracy of medical examinations, various approaches have been reported based on artificial intelligence for computer-aided diagnosis. Recently, deep learning–based approaches have been applied to many possible areas, showing greatly improved performance, especially for image-based recognition and classification. By reviewing recent deep learning–based approaches for clinical applications, we present the current status and future direction of artificial intelligence for capsule endoscopy.

[1]  Dumitru Erhan,et al.  Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[2]  Yi Li,et al.  Classifying digestive organs in wireless capsule endoscopy images based on deep convolutional neural network , 2015, 2015 IEEE International Conference on Digital Signal Processing (DSP).

[3]  David A. McAllester,et al.  Object Detection with Discriminatively Trained Part Based Models , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Guozheng Yan,et al.  Bleeding detection in wireless capsule endoscopy images based on color invariants and spatial pyramids using support vector machines , 2011, 2011 Annual International Conference of the IEEE Engineering in Medicine and Biology Society.

[5]  Antonio Torralba,et al.  Modeling the Shape of the Scene: A Holistic Representation of the Spatial Envelope , 2001, International Journal of Computer Vision.

[6]  Nikolaos G. Bourbakis,et al.  Detection of Small Bowel Polyps and Ulcers in Wireless Capsule Endoscopy Videos , 2011, IEEE Transactions on Biomedical Engineering.

[7]  P. Baldi,et al.  Deep Learning Localizes and Identifies Polyps in Real Time With 96% Accuracy in Screening Colonoscopy. , 2018, Gastroenterology.

[8]  Yixuan Yuan,et al.  Deep learning for polyp recognition in wireless capsule endoscopy images , 2017, Medical physics.

[9]  Cordelia Schmid,et al.  Scale & Affine Invariant Interest Point Detectors , 2004, International Journal of Computer Vision.

[10]  Isabel N. Figueiredo,et al.  Automated Polyp Detection in Colon Capsule Endoscopy , 2013, IEEE Transactions on Medical Imaging.

[11]  Ross B. Girshick,et al.  Reducing Overfitting in Deep Networks by Decorrelating Representations , 2015, ICLR.

[12]  D. Iakovidis,et al.  Software for enhanced video capsule endoscopy: challenges for essential progress , 2015, Nature Reviews Gastroenterology &Hepatology.

[13]  Max Q.-H. Meng,et al.  A deep convolutional neural network for bleeding detection in Wireless Capsule Endoscopy images , 2016, 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC).

[14]  Suet-Peng Yong,et al.  A comparison of deep learning and hand crafted features in medical image modality classification , 2016, 2016 3rd International Conference on Computer and Information Sciences (ICCOINS).

[15]  Michael S. Bernstein,et al.  ImageNet Large Scale Visual Recognition Challenge , 2014, International Journal of Computer Vision.

[16]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[17]  K. Chayama,et al.  Computer-aided diagnosis of colorectal polyp histology by using a real-time image recognition system and narrow-band imaging magnifying colonoscopy. , 2016, Gastrointestinal endoscopy.

[18]  G. Iddan,et al.  Wireless capsule endoscopy , 2003, Gut.

[19]  K. Mori,et al.  Real-Time Use of Artificial Intelligence in Identification of Diminutive Polyps During Colonoscopy , 2018, Annals of Internal Medicine.

[20]  Nitish Srivastava,et al.  Dropout: a simple way to prevent neural networks from overfitting , 2014, J. Mach. Learn. Res..

[21]  W. Hasler,et al.  New vision in video capsule endoscopy: current status and future directions , 2012, Nature Reviews Gastroenterology &Hepatology.

[22]  J. Saurin,et al.  A neural network algorithm for detection of GI angiectasia during small-bowel capsule endoscopy. , 2019, Gastrointestinal endoscopy.

[23]  Christopher G. Harris,et al.  A Combined Corner and Edge Detector , 1988, Alvey Vision Conference.

[24]  Fei Gao,et al.  Convolutional neural networks for intestinal hemorrhage detection in wireless capsule endoscopy images , 2017, 2017 IEEE International Conference on Multimedia and Expo (ICME).

[25]  Luc Van Gool,et al.  Speeded-Up Robust Features (SURF) , 2008, Comput. Vis. Image Underst..

[26]  Yun Jeong Lim,et al.  Current Status and Research into Overcoming Limitations of Capsule Endoscopy , 2016, Clinical endoscopy.

[27]  Matti Pietikäinen,et al.  Multiresolution Gray-Scale and Rotation Invariant Texture Classification with Local Binary Patterns , 2002, IEEE Trans. Pattern Anal. Mach. Intell..

[28]  Dimitris K. Iakovidis,et al.  Detecting and Locating Gastrointestinal Anomalies Using Deep Learning and Iterative Cluster Unification , 2018, IEEE Transactions on Medical Imaging.

[29]  Geoffrey E. Hinton,et al.  ImageNet classification with deep convolutional neural networks , 2012, Commun. ACM.

[30]  John F. Canny,et al.  A Computational Approach to Edge Detection , 1986, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[31]  Ramesh Jain,et al.  Hookworm Detection in Wireless Capsule Endoscopy Images With Deep Learning , 2018, IEEE Transactions on Image Processing.

[32]  Jordi Vitrià,et al.  Generic Feature Learning for Wireless Capsule Endoscopy Analysis , 2016, Comput. Biol. Medicine.

[33]  David G. Lowe,et al.  Object recognition from local scale-invariant features , 1999, Proceedings of the Seventh IEEE International Conference on Computer Vision.

[34]  Ali Farhadi,et al.  You Only Look Once: Unified, Real-Time Object Detection , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[35]  Jitendra Malik,et al.  Shape matching and object recognition using shape contexts , 2010, 2010 3rd International Conference on Computer Science and Information Technology.

[36]  Bill Triggs,et al.  Histograms of oriented gradients for human detection , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[37]  Guozheng Yan,et al.  Bleeding Detection in Wireless Capsule Endoscopy Based on Probabilistic Neural Network , 2011, Journal of Medical Systems.

[38]  Richard Szeliski,et al.  Computer Vision - Algorithms and Applications , 2011, Texts in Computer Science.

[39]  A. Uhl,et al.  Computer-Aided Decision Support Systems for Endoscopy in the Gastrointestinal Tract: A Review , 2011, IEEE Reviews in Biomedical Engineering.

[40]  Andrew Zisserman,et al.  Very Deep Convolutional Networks for Large-Scale Image Recognition , 2014, ICLR.

[41]  Dimitris K. Iakovidis,et al.  Automatic lesion detection in capsule endoscopy based on color saliency: closer to an essential adjunct for reviewing software. , 2014, Gastrointestinal endoscopy.

[42]  Luc Van Gool,et al.  SURF: Speeded Up Robust Features , 2006, ECCV.