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Automated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learning

Abstract

Objective: Echocardiography, a critical tool for assessing left atrial (LA) volume, often relies on manual or semi-automated measurements. This study introduces a fully automated, real-time method for measuring LA volume in both 2-D and 3-D imaging, in the aim of offering accuracy comparable to that of expert assessments while saving time and reducing operator variability. Methods: We developed an automated pipeline comprising a network to identify the end-systole (ES) time point and robust 2-D and 3-D U-Nets for segmentation. We employed data sets of 789 2-D images and 286 3-D recordings and explored various training regimes, including recurrent networks and pseudo-labeling, to estimate volume curves. Results: Our baseline results revealed an average volume difference of 2.9 mL for 2-D and 7.8 mL for 3-D, respectively, compared with manual methods. The application of pseudo-labeling to all frames in the cine loop generally led to more robust volume curves and notably improved ES measurement in cases with limited data. Conclusion: Our results highlight the potential of automated LA volume estimation in clinical practice. The proposed prototype application, capable of processing real-time data from a clinical ultrasound scanner, provides valuable temporal volume curve information in the echo lab.
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Category

Academic article

Language

English

Author(s)

  • Jieyu Hu
  • Sindre Hellum Olaisen
  • Erik Smistad
  • Håvard Dalen
  • Lasse Løvstakken

Affiliation

  • SINTEF Digital / Health Research
  • Nord Trondelag Hospital Trust
  • St. Olavs Hospital, Trondheim University Hospital
  • Norwegian University of Science and Technology

Year

2023

Published in

Ultrasound in Medicine and Biology

ISSN

0301-5629

Volume

50

Issue

1

Page(s)

47 - 56

View this publication at Norwegian Research Information Repository