Improving CCTA based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation

06/24/2019
by   Moti Freiman, et al.
4

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 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.

READ FULL TEXT
POST COMMENT

Comments

There are no comments yet.

Authors

page 7

page 23

page 24

11/29/2017

Automatic Spine Segmentation using Convolutional Neural Network via Redundant Generation of Class Labels for 3D Spine Modeling

There has been a significant increase from 2010 to 2016 in the number of...
02/01/2016

Improving Vertebra Segmentation through Joint Vertebra-Rib Atlases

Accurate spine segmentation allows for improved identification and quant...
04/01/2019

Standardized Assessment of Automatic Segmentation of White Matter Hyperintensities and Results of the WMH Segmentation Challenge

Quantification of cerebral white matter hyperintensities (WMH) of presum...
06/26/2019

Continuous Dice Coefficient: a Method for Evaluating Probabilistic Segmentations

Objective: Overlapping measures are often utilized to quantify the simil...
04/17/2021

Objective-Dependent Uncertainty Driven Retinal Vessel Segmentation

From diagnosing neovascular diseases to detecting white matter lesions, ...
06/25/2019

Learning a sparse database for patch-based medical image segmentation

We introduce a functional for the learning of an optimal database for pa...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.