Implementable Deep Learning for Multi-sequence Proton MRI Lung Segmentation: A Multi-center, Multi-vendor, and Multi-disease Study.

ABSTRACT:

Background

Recently, deep learning via convolutional neural networks (CNNs) has largely superseded conventional methods for proton (¹ H)-MRI lung segmentation. However, previous deep learning studies have utilized single-center data and limited acquisition parameters.

Purpose

Develop a generalizable CNN for lung segmentation in ¹ H-MRI, robust to pathology, acquisition protocol, vendor, and center.

Study type

Retrospective.

Population

A total of 809 ¹ H-MRI scans from 258 participants with various pulmonary pathologies (median age (range): 57 (6-85); 42% females) and 31 healthy participants (median age (range): 34 (23-76); 34% females) that were split into training (593 scans (74%); 157 participants (55%)), testing (50 scans (6%

SUBMITTER: Astley JR 

PROVIDER: S-EPMC10946727 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature