Computer-aided detection of polyps in optical colonoscopy images

We present a computer-aided detection algorithm for polyps in optical colonoscopy images. Polyps are the precursors to colon cancer. In the US alone, 14 million optical colonoscopies are performed every year, mostly to screen for polyps. Optical colonoscopy has been shown to have an approximately 25% polyp miss rate due to the convoluted folds and bends present in the colon. In this work, we present an automatic detection algorithm to detect these polyps in the optical colonoscopy images. We use a machine learning algorithm to infer a depth map for a given optical colonoscopy image and then use a detailed pre-built polyp profile to detect and delineate the boundaries of polyps in this given image. We have achieved the best recall of 84.0% and the best specificity value of 83.4%.

[1]  K. Deguchi.  Shape reconstruction from endoscope image by its shadings , 1996, 1996 IEEE/SICE/RSJ International Conference on Multisensor Fusion and Integration for Intelligent Systems (Cat. No.96TH8242).

[2]  Frans Vos,et al.  Lines of Curvature for Polyp Detection in Virtual Colonoscopy , 2006, IEEE Transactions on Visualization and Computer Graphics.

[3]  L. Seeff,et al.  How many endoscopies are performed for colorectal cancer screening? Results from CDC's survey of endoscopic capacity. , 2004, Gastroenterology.

[4]  A. M. Youssef,et al.  Automated polyp detection at CT colonography: feasibility assessment in a human population. , 2001, Radiology.

[5]  Y. Masutani,et al.  Computerized detection of colonic polyps at CT colonography on the basis of volumetric features: pilot study. , 2002, Radiology.

[6]  Yuan-Fang Wang,et al.  Toward automated model building from video in computer-assisted diagnoses in colonoscopy , 2007, SPIE Medical Imaging.

[7]  Richard Szeliski,et al.  A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms , 2001, International Journal of Computer Vision.

[8]  Sarah Gibson.  Constrained Elastic Surface Nets: Generating Smooth Surfaces from Binary Segmented Data , 1998 .

[9]  Jung-Hwan Oh,et al.  3D Reconstruction of virtual colon structures from colonoscopy images , 2014, Comput. Medical Imaging Graph..

[10]  Carlo Tomasi,et al.  A statistical 3-D pattern processing method for computer-aided detection of polyps in CT colonography , 2001, IEEE Transactions on Medical Imaging.

[11]  Xiang Li,et al.  An Improved Electronic Colon Cleansing Method for Detection of Colonic Polyps by Virtual Colonoscopy , 2006, IEEE Transactions on Biomedical Engineering.

[12]  Antonio Torralba,et al.  Building the gist of a scene: the role of global image features in recognition. , 2006, Progress in brain research.

[13]  Arie E. Kaufman,et al.  3D Surface Reconstruction from Endoscopic Videos , 2008, Visualization in Medicine and Life Sciences.

[14]  Feng Qiu,et al.  A Pipeline for Computer Aided Polyp Detection , 2006, IEEE Transactions on Visualization and Computer Graphics.

[15]  Ce Liu,et al.  Exploring new representations and applications for motion analysis , 2009 .

[16]  Baoxin Li,et al.  Circular generalized cylinder fitting for 3D reconstruction in endoscopic imaging based on MRF , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[17]  Ce Liu,et al.  Depth Transfer: Depth Extraction from Video Using Non-Parametric Sampling , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[18]  David A. Forsyth,et al.  Shape from Texture without Boundaries , 2002, International Journal of Computer Vision.

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

[20]  Antonio Torralba,et al.  SIFT Flow: Dense Correspondence across Scenes and Its Applications , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[21]  Laurent Vinet,et al.  Hierarchical region based stereo matching , 1989, Proceedings CVPR '89: IEEE Computer Society Conference on Computer Vision and Pattern Recognition.

[22]  Feng Qiu,et al.  Virtually assisted optical colonoscopy , 2008, SPIE Medical Imaging.

[23]  Jung-Hwan Oh,et al.  3D Reconstruction of Colon Segments from Colonoscopy Images , 2009, 2009 Ninth IEEE International Conference on Bioinformatics and BioEngineering.