Segmentation of Tricuspid Valve Leaflets From Transthoracic 3D Echocardiograms of Children With Hypoplastic Left Heart Syndrome Using Deep Learning
Name
fcvm-08-735587.pdf
Description
Published version
Size
3.43 MB
Format
Adobe PDF
Checksum (MD5)
58f3e1c3aca70698655bdc619c14fe9f
Author(s)
Herz, Christian
Pace, Danielle F
Nam, Hannah H
Lasso, Andras
Dinh, Patrick
Flynn, Maura
Cianciulli, Alana
Golland, Polina
Jolley, Matthew A
Date Issued
2021
Journal
Frontiers in Cardiovascular Medicine
Publisher
Frontiers Media SA
Citation
Herz, Christian, Pace, Danielle F, Nam, Hannah H, Lasso, Andras, Dinh, Patrick et al. 2021. "Segmentation of Tricuspid Valve Leaflets From Transthoracic 3D Echocardiograms of Children With Hypoplastic Left Heart Syndrome Using Deep Learning." Frontiers in Cardiovascular Medicine, 8.
Version
Final published version
Abstract
Hypoplastic left heart syndrome (HLHS) is a severe congenital heart defect in which the right ventricle and associated tricuspid valve (TV) alone support the circulation. TV failure is thus associated with heart failure, and the outcome of TV valve repair are currently poor. 3D echocardiography (3DE) can generate high-quality images of the valve, but segmentation is necessary for precise modeling and quantification. There is currently no robust methodology for rapid TV segmentation, limiting the clinical application of these technologies to this challenging population. We utilized a Fully Convolutional Network (FCN) to segment tricuspid valves from transthoracic 3DE. We trained on 133 3DE image-segmentation pairs and validated on 28 images. We then assessed the effect of varying inputs to the FCN using Mean Boundary Distance (MBD) and Dice Similarity Coefficient (DSC). The FCN with the input of an annular curve achieved a median DSC of 0.86 [IQR: 0.81–0.88] and MBD of 0.35 [0.23–0.4] mm for the merged segmentation and an average DSC of 0.77 [0.73–0.81] and MBD of 0.6 [0.44–0.74] mm for individual TV leaflet segmentation. The addition of commissural landmarks improved individual leaflet segmentation accuracy to an MBD of 0.38 [0.3–0.46] mm. FCN-based segmentation of the tricuspid valve from transthoracic 3DE is feasible and accurate. The addition of an annular curve and commissural landmarks improved the quality of the segmentations with MBD and DSC within the range of human inter-user variability. Fast and accurate FCN-based segmentation of the tricuspid valve in HLHS may enable rapid modeling and quantification, which in the future may inform surgical planning. We are now working to deploy this network for public use.
MIT Department
Massachusetts Institute of Technology. Computer Science and Artificial Intelligence Laboratory
Terms of Use
Creative Commons Attribution 4.0 International license
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DOI of Published Version
https://doi.org/10.3389/FCVM.2021.735587
