PCG-net: feature adaptive deep learning for automated head and neck organs-at-risk segmentation.

ABSTRACT:

Introduction

Radiation therapy is a common treatment option for Head and Neck Cancer (HNC), where the accurate segmentation of Head and Neck (HN) Organs-AtRisks (OARs) is critical for effective treatment planning. Manual labeling of HN OARs is time-consuming and subjective. Therefore, deep learning segmentation methods have been widely used. However, it is still a challenging task for HN OARs segmentation due to some small-sized OARs such as optic chiasm and optic nerve.

Methods

To address this challenge, we propose a parallel network architecture called PCG-Net, which incorporates both convolutional neural networks (CNN) and a Gate-Axial-Transformer (GAT) to effectively capture local information and global context. Additionally, we employ a cascade graph module (CGM) to enh

SUBMITTER: Luan S 

PROVIDER: S-EPMC10623055 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature