Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance.

Journal: International journal of computer assisted radiology and surgery

Published Date: Jun 24, 2021

Abstract

PURPOSE: The current study aimed to propose a Deep Learning (DL) and Augmented Reality (AR) based solution for a in-vivo robot-assisted radical prostatectomy (RARP), to improve the precision of a published work from our group. We implemented a two-steps automatic system to align a 3D virtual ad-hoc model of a patient's organ with its 2D endoscopic image, to assist surgeons during the procedure.

Authors

Leonardo Tanzi

DIGEP, Polytechnic University of Turin, Torino, Italy.
Pietro Piazzolla

Department of Management and Production Engineer, Politechnic University of Turin, Turin, Italy.
Francesco Porpiglia

Division of Urology, Department of Oncology, University of Turin, San Luigi Gonzaga Hospital, Orbassano, Turin, Italy.
Enrico Vezzetti

Department of Management and Production Engineer, Politechnic University of Turin, Turin, Italy.

Keywords

Augmented Reality Deep Learning Humans Image Processing, Computer-Assisted Male Neural Networks, Computer Semantics

External Resources

View on PubMed Access via DOI PubMed (34165672)

Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance.

Abstract

Authors

Keywords

External Resources

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