naa a22 4500 51080800X CHVBK 20180411083427.0 cr unu---uuuuu 180411e20130701xx s 000 0 eng 10.1007/s11548-013-0814-x doi (NATIONALLICENCE)springer-10.1007/s11548-013-0814-x A robust method to track colonoscopy videos with non-informative images [Elektronische Daten] [Jianfei Liu, Kalpathi Subramanian, Terry Yoo] Purpose: Continuously, optical and virtual image alignment can significantly supplement the clinical value of colonoscopy. However, the co-alignment process is frequently interrupted by non-informative images. A video tracking framework to continuously track optical colonoscopy images was developed and tested. Methods: A video tracking framework with immunity to non-informative images was developed with three essential components: temporal volume flow, region flow, and incremental egomotion estimation. Temporal volume flow selects two similar images interrupted by non-informative images; region flow measures large visual motion between selected images; and incremental egomotion processing estimates significant camera motion by decomposing each large visual motion vector into a sequence of small optical flow vectors. The framework was extensively evaluated via phantom and colonoscopy image sequences. We constructed two colon-like phantoms, a straight phantom and a curved phantom, to measure actual colonoscopy motion. Results: In the straight phantom, after 48 frames were excluded, the tracking error was $$<$$ 3mm of 16mm traveled. In the curved phantom, the error was $$<$$ 4mm of 23.88mm traveled after 72 frames were excluded. Through evaluations with clinical sequences, the robustness of the tracking framework was demonstrated on 30 colonoscopy image sequences from 22 different patients. Four specific sequences among these were chosen to illustrate the algorithm's decreased sensitivity to (1) fluid immersion, (2) wall contact, (3) surgery-induced colon deformation, and (4) multiple non-informative image sequences. Conclusion: A robust tracking framework for real-time colonoscopy was developed that facilitates continuous alignment of optical and virtual images, immune to non-informative images that enter the video stream. The system was validated in phantom testing and achieved success with clinical image sequences. CARS, 2013 Colonoscopy nationallicence Tracking nationallicence Region flow nationallicence Temporal volume flow nationallicence Egomotion nationallicence Liu Jianfei Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 20892, Bethesda, MD, USA aut Subramanian Kalpathi Department of Computer Science, The University of North Carolina at Charlotte, 28223, Charlotte, NC, USA aut Yoo Terry Office of High Performance Computing and Communications, National Library of Medicine, National Institutes of Health, 20894, Bethesda, MD, USA aut International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/4(2013-07-01), 575-592 1861-6410 8:4<575 2013 8 11548 https://doi.org/10.1007/s11548-013-0814-x text/html Onlinezugriff via DOI 1 research-article jats NATIONALLICENCE 856 40 https://doi.org/10.1007/s11548-013-0814-x text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Liu Jianfei Imaging Biomarkers and Computer-Aided Diagnosis Laboratory, Radiology and Imaging Sciences, Clinical Center, National Institutes of Health, 20892, Bethesda, MD, USA aut NATIONALLICENCE 700 1- Subramanian Kalpathi Department of Computer Science, The University of North Carolina at Charlotte, 28223, Charlotte, NC, USA aut NATIONALLICENCE 700 1- Yoo Terry Office of High Performance Computing and Communications, National Library of Medicine, National Institutes of Health, 20894, Bethesda, MD, USA aut NATIONALLICENCE 773 0- International Journal of Computer Assisted Radiology and Surgery Springer Berlin Heidelberg 8/4(2013-07-01), 575-592 1861-6410 8:4<575 2013 8 11548 Metadata rights reserved Springer special CC-BY-NC licence nationallicence BK010053 XK010053 XK010000 NATIONALLICENCE NATIONALLICENCE NL-springer