Deep Learning and Computer Vision methods for Augmented Reality solutions in robot-assisted laparoscopic surgery = Deep Learning and Computer Vision methods for Augmented Reality solutions in robot-assisted laparoscopic surgery
Erica Padovan
Deep Learning and Computer Vision methods for Augmented Reality solutions in robot-assisted laparoscopic surgery = Deep Learning and Computer Vision methods for Augmented Reality solutions in robot-assisted laparoscopic surgery.
Rel. Pietro Piazzolla, Leonardo Tanzi. Politecnico di Torino, Corso di laurea magistrale in Ingegneria Biomedica, 2021
Abstract
Objective: This thesis focuses on applications of Augmented Reality to support the surgeon during laparoscopic Robot-Assisted procedures. These applications’ goal is to augment the surgeon’s perception of the surgical scene, which is usually more limited than in open procedures. After extensive literature analysis, both in the current methodologies for 6D pose estimation and in the specific context of its application to augment reality in laparoscopic surgery, a novel methodology is here proposed for a real-time pose estimation strategy to superimpose the preoperative anatomical virtual 3D model of the kidney on the endoscopic view. Methods: The proposed methodology is divided into two subsequent steps.
The first step is based on a Deep Learning approach and exploits two Convolutional Neural Networks, one to perform the organ detection to track the organ’s position and the other to solve the initial organ’s rotation inferring problem as a classification problem
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