title: Dense soft tissue 3D reconstruction refined with super-pixel segmentation for robotic abdominal surgery.
abstract: OBJECTIVE
  Single-incision laparoscopic surgery decreases postoperative infections, but introduces limitations in the surgeon's maneuverability and in the surgical field of view. This work aims at enhancing intra-operative surgical visualization by exploiting the 3D information about the surgical site. An interactive guidance system is proposed wherein the pose of preoperative tissue models is updated online. A critical process involves the intra-operative acquisition of tissue surfaces. It can be achieved using stereoscopic imaging and 3D reconstruction techniques. This work contributes to this process by proposing new methods for improved dense 3D reconstruction of soft tissues, which allows a more accurate deformation identification and facilitates the registration process.
  
  
  METHODS
  Two methods for soft tissue 3D reconstruction are proposed: Method 1 follows the traditional approach of the block matching algorithm. Method 2 performs a nonparametric modified census transform to be more robust to illumination variation. The simple linear iterative clustering (SLIC) super-pixel algorithm is exploited for disparity refinement by filling holes in the disparity images.
  
  
  RESULTS
  The methods were validated using two video datasets from the Hamlyn Centre, achieving an accuracy of 2.95 and 1.66 mm, respectively. A comparison with ground-truth data demonstrated the disparity refinement procedure: (1) increases the number of reconstructed points by up to 43 % and (2) does not affect the accuracy of the 3D reconstructions significantly.
  
  
  CONCLUSIONS
  Both methods give results that compare favorably with the state-of-the-art methods. The computational time constraints their applicability in real time, but can be greatly improved by using a GPU implementation.
authors.affiliation: Department of Advanced Robotics, Istituto Italiano di Tecnologia, via Morego, 30, 16163, Genoa, Italy. veronica.penza@iit.it.|Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.zza L. Da Vinci, 32, 20133, Milan, Italy. veronica.penza@iit.it.
authors.forename: Veronica
authors.initials: V
authors.lastname: Penza
authors.affiliation: Department of Advanced Robotics, Istituto Italiano di Tecnologia, via Morego, 30, 16163, Genoa, Italy. jesus.ortiz@iit.it.
authors.forename: Jesús
authors.initials: J
authors.lastname: Ortiz
authors.affiliation: Department of Advanced Robotics, Istituto Italiano di Tecnologia, via Morego, 30, 16163, Genoa, Italy. leonardo.demattos@iit.it.
authors.forename: Leonardo S
authors.initials: LS
authors.lastname: Mattos
authors.affiliation: Ospedale Niguarda Ca' Granda, P.zza Dell'Ospedale Maggiore, 3, 20162, Milan, Italy. antonello.forgione@ospedaleniguarda.it.|AIMS Academy, P.zza Dell'Ospedale Maggiore, 3, 20162, Milan, Italy. antonello.forgione@ospedaleniguarda.it.|Valuebiotech s.r.l., P.zza Dell'Ospedale Maggiore, 3, 20162, Milan, Italy. antonello.forgione@ospedaleniguarda.it.
authors.forename: Antonello
authors.initials: A
authors.lastname: Forgione
authors.affiliation: Department of Electronics, Information and Bioengineering, Politecnico di Milano, P.zza L. Da Vinci, 32, 20133, Milan, Italy. elena.demomi@polimi.it.
authors.forename: Elena
authors.initials: E
authors.lastname: De Momi
mesh_terms: D000005:Abdomen / Q000601:surgery*; D000465:Algorithms; D006801:Humans; D021621:Imaging, Three-Dimensional / Q000379:methods*; D010535:Laparoscopy / Q000379:methods*; D012371:Robotics / Q000379:methods*; D025321:Surgery, Computer-Assisted / Q000379:methods*
keywords: Census transform; Depth estimation; Robotic surgery; Super-pixel segmentation; Surface reconstruction