Application of deep learning for semantic segmentation in robotic prostatectomy: Comparison of convolutional neural networks and visual transformers.

ABSTRACT:

Purpose

Semantic segmentation is a fundamental part of the surgical application of deep learning. Traditionally, segmentation in vision tasks has been performed using convolutional neural networks (CNNs), but the transformer architecture has recently been introduced and widely investigated. We aimed to investigate the performance of deep learning models in segmentation in robot-assisted radical prostatectomy (RARP) and identify which of the architectures is superior for segmentation in robotic surgery.

Materials and methods

Intraoperative images during RARP were obtained. The dataset was randomly split into training and validation data. Segmentation of the surgical instruments, bladder, prostate, vas and seminal vesicle was performed using three CNN models (DeepLabv3, MANet,

SUBMITTER: Pak S 

PROVIDER: S-EPMC11543645 | biostudies-literature | 2024 Nov

REPOSITORIES: biostudies-literature