Xiaofei Du1, Neil Clancy2, Shobhit Arya3, George B Hanna4, John Kelly5, Daniel S Elson6, Danail Stoyanov7. 1. Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK. xiaofei.du.13@ucl.ac.uk. 2. Department of Surgery and Cancer, Imperial College London, London, UK. n.clancy@imperial.ac.uk. 3. Department of Surgery and Cancer, Imperial College London, London, UK. 4. Department of Surgery and Cancer, Imperial College London, London, UK. g.hanna@imperial.ac.uk. 5. Division of Surgery and Interventional Science, University College London, London, UK. 6. Department of Surgery and Cancer, Imperial College London, London, UK. daniel.elson@imperial.ac.uk. 7. Department of Computer Science, Centre for Medical Image Computing, University College London, London, UK. danail.stoyanov@ucl.ac.uk.
Abstract
PURPOSE: Recovering tissue deformation during robotic-assisted minimally invasive surgery procedures is important for providing intra-operative guidance, enabling in vivo imaging modalities and enhanced robotic control. The tissue motion can also be used to apply motion stabilization and to prescribe dynamic constraints for avoiding critical anatomical structures. METHODS: Image-based methods based independently on salient features or on image intensity have limitations when dealing with homogeneous soft tissues or complex reflectance. In this paper, we use a triangular geometric mesh model in order to combine the advantages of both feature and intensity information and track the tissue surface reliably and robustly. RESULTS: Synthetic and in vivo experiments are performed to provide quantitative analysis of the tracking accuracy of our method, and we also show exemplar results for registering multispectral images where there is only a weak image signal. CONCLUSION: Compared to traditional methods, our hybrid tracking method is more robust and has improved convergence in the presence of larger displacements, tissue dynamics and illumination changes.
PURPOSE: Recovering tissue deformation during robotic-assisted minimally invasive surgery procedures is important for providing intra-operative guidance, enabling in vivo imaging modalities and enhanced robotic control. The tissue motion can also be used to apply motion stabilization and to prescribe dynamic constraints for avoiding critical anatomical structures. METHODS: Image-based methods based independently on salient features or on image intensity have limitations when dealing with homogeneous soft tissues or complex reflectance. In this paper, we use a triangular geometric mesh model in order to combine the advantages of both feature and intensity information and track the tissue surface reliably and robustly. RESULTS: Synthetic and in vivo experiments are performed to provide quantitative analysis of the tracking accuracy of our method, and we also show exemplar results for registering multispectral images where there is only a weak image signal. CONCLUSION: Compared to traditional methods, our hybrid tracking method is more robust and has improved convergence in the presence of larger displacements, tissue dynamics and illumination changes.
Authors: L Maier-Hein; P Mountney; A Bartoli; H Elhawary; D Elson; A Groch; A Kolb; M Rodrigues; J Sorger; S Speidel; D Stoyanov Journal: Med Image Anal Date: 2013-05-03 Impact factor: 8.545
Authors: Neil T Clancy; Danail Stoyanov; David R C James; Aimee Di Marco; Vincent Sauvage; James Clark; Guang-Zhong Yang; Daniel S Elson Journal: Biomed Opt Express Date: 2012-09-14 Impact factor: 3.732
Authors: Neil T Clancy; Shobhit Arya; Danail Stoyanov; Mohan Singh; George B Hanna; Daniel S Elson Journal: Biomed Opt Express Date: 2015-09-30 Impact factor: 3.732
Authors: Srdjan Saso; Neil T Clancy; Benjamin P Jones; Timothy Bracewell-Milnes; Maya Al-Memar; Eleanor M Cannon; Simran Ahluwalia; Joseph Yazbek; Meen-Yau Thum; Tom Bourne; Daniel S Elson; James Richard Smith; Sadaf Ghaem-Maghami Journal: Future Sci OA Date: 2018-02-06