Towards Multi-Modal Image-Guided Tumour Identification in Robot-Assisted Partial Nephrectomy
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1 Towards Multi-Modal Image-Guided Tumour Identification in Robot-Assisted Partial Nephrectomy Ghassan Hamarneh 1, Alborz Amir-Khalili 2, Masoud Nosrati 1, Ivan Figueroa 2, Jeremy Kawahara 1, Osama Al-Alao 3, Jean-Marc Peyrat 4, Julien Abi-Nahed 4, Abdulla Al-Ansari 3, Rafeef Abugharbieh 2 1 Medical Image Analysis Lab, Simon Fraser University, Burnaby, Canada 2 Biomedical Signal and Image Computing Lab, University of British Columbia, Vancouver, Canada 3 Urology Department, Hamad Medical Corporation, Doha, Qatar 4 Qatar Robotic Surgery Centre, Qatar Science & Technology Park, Doha, Qatar
2 Overview 1. Robot-Assisted Partial Nephrectomy (RAPN) 2. Image-Guidance System for Robotic Surgery 3. Conclusion 2"
3 Overview 1.Robot-Assisted Partial Nephrectomy (RAPN) 2. Image-Guidance System for Robotic Surgery 3. Conclusion 3"
4 1.1. Kidney & Cancer! Kidney anatomy! Kidney function: filter blood Cortex Medulla Renal Artery Renal Vein Ureter! Kidney cancer: Tumour Kidney 4"
5 1.2. Partial vs. Radical Nephrectomy! Renal mass > 4cm => radical nephrectomy + simple surgical procedure! Renal mass < 4cm => partial nephrectomy + avoidance of renal insufficiency - possible recurrence " Challenge: Remove all cancerous tissue while sparing as much healthy tissue as possible to preserve kidney function and avoid recurrence?" 5"
6 1.3. Steps of Partial Nephrectomy Courtesy of Intuitive Surgical Inc. 6"
7 1.4. Open vs. Minimally Invasive Surgery + Less blood loss + Less infections + Faster recovery time + Esthetic For robotic surgery: + dexterity/precision + ergonomy + enhanced 3D vision 7"
8 Slave" 1.5. Robotic Surgery Master" Stereo4Endoscopic"" Camera" Surgeon s"console"view" Augmented"Reality" Pre4Opera7ve"Scan"(CT/MR)" Intra4Opera7ve"US" 8"
9 Overview 1. Robot-Assisted Partial Nephrectomy (RAPN) 2.Image-Guidance System for Robotic Surgery 3. Conclusion 9"
10 2. Image-Guidance System for Robotic Surgery Segmenta7on" Pre4Opera7ve"CT/MR" Stereo"Endoscopic"Video" Laparoscopic"US" 10"
11 2.1. Pre-Operative CT Segmentation " Localize structures of interest in pre-operative scans # Semi-automatic probabilistic segmentation based on Random Walker [Grady,2006]! Input: seed points for each class (kidney, tumour, background)! Output: probability map to belong to a class => Segmentation + Uncertainties/Confidence CT"Scan" Probabilis9c"Segmenta9on"of" kidney,"tumour,"and"background"" 11"
12 2. Image-Guidance System for Robotic Surgery Biomechanical"Modeling" Segmenta7on" Pre4Opera7ve"CT/MR" Stereo"Endoscopic"Video" Laparoscopic"US" 12"
13 2.2. Biomechanical Modeling of Deformations " Predict deformations between pre-operative scan and surgery due to change of:! patient position (gravity)! pressure during insufflation (surfacic force load) # Finite element method (FEM) using SOFA platform (with corotational tetrahedral formulation and a Eulerian implicit solver) where u = displacement M = mass C = stiffness K = damping F = external force => Improvement of tumour localization prediction by 29% 13"
14 2. Image-Guidance System for Robotic Surgery Biomechanical"Modeling" Segmenta7on" Surface" Reconstruc7on" Pre4Opera7ve"CT/MR" Stereo"Endoscopic"Video" Laparoscopic"US" 14"
15 2.3. Stereo Surface Reconstruction " Build"surface"of"actual"surgical"scene"" from"stereo"endoscopic"camera" " # Improvement"of"robustness"[Bernhardt,2012]" # Improvement"of"accuracy"(+56%)"" using"shape"prior"[amir,2013]" LeU"camera" w/o"shape"prior" w/"shape"prior" # Improvement"of"computa9on"speed"(up"to"x56)"with"GPU"[Islam,2013]" 15"
16 2. Image-Guidance System for Robotic Surgery Mul74Modal"Registra7on" Biomechanical"Modeling" Segmenta7on" Surface" Reconstruc7on" Pre4Opera7ve"CT/MR" Stereo"Endoscopic"Video" Laparoscopic"US" 16"
17 2.4. Pre- and Intra-Operative Image Registration " Align in the same reference frame CT segmentation and stereo endoscopic video # Match 3D model projection with boundaries of kidney and tumour in left and right camera Ini9al"Pose" Rigid"Registra9on" Deformable"Registra9on" Video"Sequence" 17"
18 2.4. Pre- and Intra-Operative Image Registration DSC Our method ACWOE DSC Robustness to noise compared to active contours without edges (ACWOE) 0.65 R L LN LNC Accuracy (DSC=dice score) between rigid, linear, non-linear, non-linear + cam. param. 18"
19 2. Image-Guidance System for Robotic Surgery Augmented"Reality"Visualiza7on" Mul74Modal"Registra7on" Biomechanical"Modeling" Segmenta7on" Surface" Reconstruc7on" Pre4Opera7ve"CT/MR" Stereo"Endoscopic"Video" Laparoscopic"US" 19"
20 2.5. Augmented Reality Visualization " Guide surgeon in localizing tumour boundaries # Simple way to display uncertainties without obstructing surgeon s view Mesh"of"most"" probable"boundary" Most"probable"contour" Range"of"iso[probabili9es" Color[coded"uncertain9es" [Amir,2013]"" 20"
21 Overview 1. Robot-Assisted Partial Nephrectomy (RAPN) 2. Image-Guidance System for Robotic Surgery 3.Conclusion 21"
22 3. Conclusion Summary of Contributions: # Proof-of-concept framework for uncertainty encoded augmented reality system that fuses pre-operative data into surgical scene # Collection of valuable patient data in the context of RAPN: CT/MR/US + laparoscopic US + stereo video + annotations Future Work: " Use laparoscopic US " Compute and fuse uncertainties from all steps " Biomechanical modeling during surgery " Full system integration and validation 22"
23 Acknowledgements This work was supported by NPRP Grant # from the Qatar National Research Fund (a member of the Qatar Foundation). The statements made herein are solely the responsibility of the authors. 23"
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