Towards Patient-Specific Surgical Planning for Bicuspid Aortic Valve Repair: Fully Automated Segmentation of the Aortic Valve in 4D CT
Journal:
arXiv
Published Date:
Feb 13, 2025
Abstract
The bicuspid aortic valve (BAV) is the most prevalent congenital heart defect
and may require surgery for complications such as stenosis, regurgitation, and
aortopathy. BAV repair surgery is effective but challenging due to the
heterogeneity of BAV morphology. Multiple imaging modalities can be employed to
assist the quantitative assessment of BAVs for surgical planning.
Contrast-enhanced 4D computed tomography (CT) produces volumetric temporal
sequences with excellent contrast and spatial resolution. Segmentation of the
aortic cusps and root in these images is an essential step in creating patient
specific models for visualization and quantification. While deep learning-based
methods are capable of fully automated segmentation, no BAV-specific model
exists. Among valve segmentation studies, there has been limited quantitative
assessment of the clinical usability of the segmentation results. In this work,
we developed a fully automated multi-label BAV segmentation pipeline based on
nnU-Net. The predicted segmentations were used to carry out surgically relevant
morphological measurements including geometric cusp height, commissural angle
and annulus diameter, and the results were compared against manual
segmentation. Automated segmentation achieved average Dice scores of over 0.7
and symmetric mean distance below 0.7 mm for all three aortic cusps and the
root wall. Clinically relevant benchmarks showed good consistency between
manual and predicted segmentations. Overall, fully automated BAV segmentation
of 3D frames in 4D CT can produce clinically usable measurements for surgical
risk stratification, but the temporal consistency of segmentations needs to be
improved.
