Automated lung segmentation on chest MRI in children with cystic fibrosis.

ABSTRACT:

Introduction

Segmentation of lung structures in medical imaging is crucial for the application of automated post-processing steps on lung diseases like cystic fibrosis (CF). Recently, machine learning methods, particularly neural networks, have demonstrated remarkable improvements, often outperforming conventional segmentation methods. Nonetheless, challenges still remain when attempting to segment various imaging modalities and diseases, especially when the visual characteristics of pathologic findings significantly deviate from healthy tissue.

Methods

Our study focuses on imaging of pediatric CF patients [mean age, standard deviation (7.50 ± 4.6)], utilizing deep learning-based methods for automated lung segmentation from chest magnetic resonance imaging (MRI). A total of

SUBMITTER: Ringwald FG 

PROVIDER: S-EPMC11600534 | biostudies-literature | 2024

REPOSITORIES: biostudies-literature