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. 2021 Dec 9;8:735587. doi: 10.3389/fcvm.2021.735587

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Copyright © 2021 Herz, Pace, Nam, Lasso, Dinh, Flynn, Cianciulli, Golland and Jolley.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

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Examples of Segmentations predicted by the FCN. From Left to Right: 2D view of the MS frame (including MBD displayed at the bottom) and 3D segmentation model (ventricular view including indicator of 2D slice location) for Manual Segmentation, FCN + Annulus (Ann), and FCN + Ann + Commissures (Com). From Top to Bottom: 95th, 75th, 50th, 25th, and 5th percentile for MBD based on the best ranked CSP FCN with Ann + Com. The bottom row (5th percentile) shows an example of ultrasound signal dropout (indicated by arrows) that influenced the inferred segmentation using the annular curve only relative to the manual segmentation in which the user enforced the topology. When using the commissural landmarks in addition to the annular curve as FCN inputs, individual leaflet boundary detection improved and FCN segmentation was less sensitive to signal dropouts.