Deep Learning Model for Real‑time Semantic Segmentation During Intraoperative Robotic Prostatectomy.

ABSTRACT:

Background and objective

Recently, deep learning algorithms, including convolutional neural networks (CNNs), have shown remarkable progress in medical imaging analysis. Semantic segmentation, which segments an unknown image into different parts and objects, has potential applications in robotic surgery in areas where artificial intelligence (AI) can be applied, such as in AI-assisted surgery, surgeon training, and skill assessment. We aimed to investigate the performance of a CNN-based deep learning model in real-time segmentation in robot-assisted radical prostatectomy (RALP).

Methods

Intraoperative videos of RALP procedures were obtained. The reinforcement U-Net model was used for segmentation. Segmentation of the images of instruments, bladder, prostate, and seminal vesic

SUBMITTER: Gon Park S 

PROVIDER: S-EPMC10998267 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature