#5968. Real-time deep learning semantic segmentation during intra-operative surgery for 3D augmented reality assistance

August 2026publication date
Proposal available till 07-06-2025
4 total number of authors per manuscript0 $

The title of the journal is available only for the authors who have already paid for
Journal’s subject area:
Surgery;
Computer Graphics and Computer-Aided Design;
Radiology, Nuclear Medicine and Imaging;
Health Informatics;
Computer Science Applications;
Computer Vision and Pattern Recognition;
Biomedical Engineering;
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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. Methods: This approach was carried out using a Convolutional Neural Network (CNN) based structure for semantic segmentation and a subsequent elaboration of the obtained output, which produced the needed parameters for attaching the 3D model. We used a dataset obtained from 5 endoscopic videos (A, B, C, D, E), selected and tagged by our team’s specialists. We then evaluated the most performing couple of segmentation architecture and neural network and tested the overlay performances.
Keywords:
Deep learning; Intra-operative; Neural network; Semantic segmentation

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