Computer-assisted polyp matching between optical colonoscopy and CT colonography: a phantom study

Potentially precancerous polyps detected with CT colonography (CTC) need to be removed subsequently, using an optical colonoscope (OC). Due to large colonic deformations induced by the colonoscope, even very experienced colonoscopists find it difficult to pinpoint the exact location of the colonoscope tip in relation to polyps reported on CTC. This can cause unduly prolonged OC examinations that are stressful for the patient, colonoscopist and supporting staff. We developed a method, based on monocular 3D reconstruction from OC images, that automatically matches polyps observed in OC with polyps reported on prior CTC. A matching cost is computed, using rigid point-based registration between surface point clouds extracted from both modalities. A 3D printed and painted phantom of a 25 cm long transverse colon segment was used to validate the method on two medium sized polyps. Results indicate that the matching cost is smaller at the correct corresponding polyp between OC and CTC: the value is 3.9 times higher at the incorrect polyp, comparing the correct match between polyps to the incorrect match. Furthermore, we evaluate the matching of the reconstructed polyp from OC with other colonic endoluminal surface structures such as haustral folds and show that there is a minimum at the correct polyp from CTC. Automated matching between polyps observed at OC and prior CTC would facilitate the biopsy or removal of true-positive pathology or exclusion of false-positive CTC findings, and would reduce colonoscopy false-negative (missed) polyps. Ultimately, such a method might reduce healthcare costs, patient inconvenience and discomfort.

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

[2]  Jianfei Liu,et al.  An optical flow approach to tracking colonoscopy video , 2013, Comput. Medical Imaging Graph..

[3]  Andriy Myronenko,et al.  Point Set Registration: Coherent Point Drift , 2009, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[4]  Yuan-Fang Wang,et al.  Feature detector and descriptor for medical images , 2009, Medical Imaging.

[5]  Sun Young Park,et al.  Image-based camera motion estimation using prior probabilities , 2011, Medical Imaging.

[6]  Marc Modat,et al.  Registration of the endoluminal surfaces of the colon derived from prone and supine CT colonography. , 2011, Medical physics.

[7]  Joni-Kristian Kämäräinen,et al.  Narrow Baseline GLSL Multiview Stereo , 2010 .

[8]  Jia Gu,et al.  Computer-aided diagnosis (CAD) for colonoscopy , 2007, Medical Imaging: Computer-Aided Diagnosis.

[9]  Bernhard P. Wrobel,et al.  Multiple View Geometry in Computer Vision , 2001 .

[10]  D. Rex,et al.  Colonoscopic miss rates of adenomas determined by back-to-back colonoscopies. , 1997, Gastroenterology.

[11]  Ruigang Yang,et al.  A versatile stereo implementation on commodity graphics hardware , 2005, Real Time Imaging.

[12]  Jung-Hwan Oh,et al.  Colon fold contour estimation for 3D visualization of colon structure from 2D colonoscopy images , 2011, 2011 IEEE International Symposium on Biomedical Imaging: From Nano to Macro.

[13]  Jianfei Liu,et al.  A stable optic-flow based method for tracking colonoscopy images , 2008, 2008 IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops.

[14]  Yuan-Fang Wang,et al.  Uniscale multi-view registration using double dog-leg method , 2009, Medical Imaging.

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

[16]  Holger Lange,et al.  Computer-aided-diagnosis (CAD) for colposcopy , 2005, SPIE Medical Imaging.

[17]  Robert T. Collins,et al.  A space-sweep approach to true multi-image matching , 1996, Proceedings CVPR IEEE Computer Society Conference on Computer Vision and Pattern Recognition.