dc.contributor.author | Hu, Jieyu | |
dc.contributor.author | Olaisen, Sindre Hellum | |
dc.contributor.author | Smistad, Erik | |
dc.contributor.author | Dalen, Håvard | |
dc.contributor.author | Løvstakken, Lasse | |
dc.date.accessioned | 2024-05-31T08:09:40Z | |
dc.date.available | 2024-05-31T08:09:40Z | |
dc.date.created | 2023-10-20T12:48:24Z | |
dc.date.issued | 2023 | |
dc.identifier.citation | Ultrasound in Medicine and Biology. 2023, 50 (1), 47-56. | en_US |
dc.identifier.issn | 0301-5629 | |
dc.identifier.uri | https://hdl.handle.net/11250/3132064 | |
dc.description.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. | en_US |
dc.language.iso | eng | en_US |
dc.publisher | Elsevier | en_US |
dc.rights | Navngivelse 4.0 Internasjonal | * |
dc.rights.uri | http://creativecommons.org/licenses/by/4.0/deed.no | * |
dc.title | Automated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learning | en_US |
dc.title.alternative | Automated 2-D and 3-D Left Atrial Volume Measurements Using Deep Learning | en_US |
dc.type | Journal article | en_US |
dc.type | Peer reviewed | en_US |
dc.description.version | publishedVersion | en_US |
dc.source.pagenumber | 47-56 | en_US |
dc.source.volume | 50 | en_US |
dc.source.journal | Ultrasound in Medicine and Biology | en_US |
dc.source.issue | 1 | en_US |
dc.identifier.doi | 10.1016/j.ultrasmedbio.2023.08.024 | |
dc.identifier.cristin | 2186748 | |
dc.relation.project | Norges forskningsråd: 237887 | en_US |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 2 | |