Fully automatic cardiac four chamber and great vessel segmentation on CT pulmonary angiography using deep learning.

ABSTRACT:

Introduction

Computed tomography pulmonary angiography (CTPA) is an essential test in the work-up of suspected pulmonary vascular disease including pulmonary hypertension and pulmonary embolism. Cardiac and great vessel assessments on CTPA are based on visual assessment and manual measurements which are known to have poor reproducibility. The primary aim of this study was to develop an automated whole heart segmentation (four chamber and great vessels) model for CTPA.

Methods

A nine structure semantic segmentation model of the heart and great vessels was developed using 200 patients (80/20/100 training/validation/internal testing) with testing in 20 external patients. Ground truth segmentations were performed by consultant cardiothoracic radiologists. Failure analysis was co

SUBMITTER: Sharkey MJ 

PROVIDER: S-EPMC9549370 | biostudies-literature | 2022

REPOSITORIES: biostudies-literature