2 research outputs found
Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography
Detection of coronary artery stenosis in coronary CT angiography (CCTA)
requires highly personalized surface meshes enclosing the coronary lumen. In
this work, we propose to use graph convolutional networks (GCNs) to predict the
spatial location of vertices in a tubular surface mesh that segments the
coronary artery lumen. Predictions for individual vertex locations are based on
local image features as well as on features of neighboring vertices in the mesh
graph. The method was trained and evaluated using the publicly available
Coronary Artery Stenoses Detection and Quantification Evaluation Framework.
Surface meshes enclosing the full coronary artery tree were automatically
extracted. A quantitative evaluation on 78 coronary artery segments showed that
these meshes corresponded closely to reference annotations, with a Dice
similarity coefficient of 0.75/0.73, a mean surface distance of 0.25/0.28 mm,
and a Hausdorff distance of 1.53/1.86 mm in healthy/diseased vessel segments.
The results showed that inclusion of mesh information in a GCN improves
segmentation overlap and accuracy over a baseline model without interaction on
the mesh. The results indicate that GCNs allow efficient extraction of coronary
artery surface meshes and that the use of GCNs leads to regular and more
accurate meshes.Comment: MICCAI 2019 Workshop on Graph Learning in Medical Image (GLMI
Improving CCTA based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation
Purpose: The goal of this study was to assess the potential added benefit of
accounting for partial volume effects (PVE) in an automatic coronary lumen
segmentation algorithm from coronary computed tomography angiography (CCTA).
Materials and methods: We assessed the potential added value of PVE integration
as a part of the automatic coronary lumen segmentation algorithm by means of
segmentation accuracy using the MICCAI 2012 challenge framework and by means of
flow simulation overall accuracy, sensitivity, specificity, negative and
positive predictive values and the receiver operated characteristic (ROC) area
under the curve. We also evaluated the potential benefit of accounting for PVE
in automatic segmentation for flow-simulation for lesions that were diagnosed
as obstructive based on CCTA, which could have indicated a need for an invasive
exam and revascularization. Results: Our segmentation algorithm improves the
maximal surface distance error by ~39% compared to previously published method
on the 18 datasets 50 from the MICCAI 2012 challenge with comparable Dice and
mean surface distance. Results with and without accounting for PVE were
comparable. In contrast, integrating PVE analysis into an automatic coronary
lumen segmentation algorithm improved the flow simulation specificity from 0.6
to 0.68 with the same sensitivity of 0.83. Also, accounting for PVE improved
the area under the ROC curve for detecting hemodynamically significant CAD from
0.76 to 0.8 compared to automatic segmentation without PVE analysis with
invasive FFR threshold of 0.8 as the reference standard. The improvement in the
AUC was statistically significant (N=76, Delong's test, p=0.012). Conclusion:
Accounting for the partial volume effects in automatic coronary lumen
segmentation algorithms has the potential to improve the accuracy of CCTA-based
hemodynamic assessment of coronary artery lesions