Improving <scp>CCTA</scp>‐based lesions' hemodynamic significance assessment by accounting for partial volume modeling in automatic coronary lumen segmentation

Freiman, Moti; Nickisch, Hannes; Prevrhal, Sven; Schmitt, Holger; Vembar, Mani; Maurovich-Horvat, Pál; Donnelly, Patrick; Goshen, Liran

doi:10.1002/mp.12121

Cited by 24 publications

(20 citation statements)

References 32 publications

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“…We generated the 3D coronary tree model using the coronary lumen segmentation algorithm of Freiman et al with manual adjustments where required (M.V., P.M.H., P.D. ).…”

Section: Methodsmentioning

confidence: 99%

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A functionally personalized boundary condition model to improve estimates of fractional flow reserve with CT (CT‐FFR)

et al. 2018

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“…We generated the 3D coronary tree model using the coronary lumen segmentation algorithm of Freiman et al with manual adjustments where required (M.V., P.M.H., P.D. ).…”

Section: Methodsmentioning

confidence: 99%

“…Existing algorithms can be used to generate accurate patient‐specific 3D models of the coronary tree with manual adjustment where required. Computational fluid dynamics (CFD) methods, reduced order models or learned zero‐dimensional (0D) lumped models can solve the governing flow equations.…”

Section: Introductionmentioning

confidence: 99%

A functionally personalized boundary condition model to improve estimates of fractional flow reserve with CT (CT‐FFR)

et al. 2018

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“…We apply the proposed approach to reduce the database size required for the coronary lumen segmentation algorithm proposed by Freiman et al [3]. For the sake of completeness, we briefly describe the relevant parts of the algorithm here.…”

Section: Coronary Lumen Segmentationmentioning

confidence: 99%

“…For the sake of completeness, we briefly describe the relevant parts of the algorithm here. We refer the interested reader to a detailed and complete description provided in [3]. The algorithm formulates the segmentation task as an energy minimization problem over a cylindrical coordinate system [6] where the warped volume along the coronary artery centerline is expressed with the coordinate i representing the index of the cross-sectional plane, and θ, r represent the angle and the radial distance determining a point in the cross-sectional plane…”

Section: Coronary Lumen Segmentationmentioning

confidence: 99%

Learning a Sparse Database for Patch-Based Medical Image Segmentation

Freiman

Nickisch

Schmitt

et al. 2017

Patch-Based Techniques in Medical Imaging

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We introduce a functional for the learning of an optimal database for patch-based image segmentation with application to coronary lumen segmentation from coronary computed tomography angiography (CCTA) data. The proposed functional consists of fidelity, sparseness and robustness to small-variations terms and their associated weights. Existing work address database optimization by prototype selection aiming to optimize the database by either adding or removing prototypes according to a set of predefined rules. In contrast, we formulate the database optimization task as an energy minimization problem that can be solved using standard numerical tools. We apply the proposed database optimization functional to the task of optimizing a database for patch-base coronary lumen segmentation. Our experiments using the publicly available MICCAI 2012 coronary lumen segmentation challenge data show that optimizing the database using the proposed approach reduced database size by 96% while maintaining the same level of lumen segmentation accuracy. Moreover, we show that the optimized database yields an improved specificity of CCTA based fractional flow reserve (0.73 vs 0.7 for all lesions and 0.68 vs 0.65 for obstructive lesions) using a training set of 132 (76 obstructive) coronary lesions with invasively measured FFR as the reference.

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“…Methods for stenosis detection [5] and blood flow simulation [12] typically require highly personalized coronary lumen surface meshes with sub-voxel accuracy. Because manual segmentation of the full coronary artery tree in a CCTA image would hardly be feasible, such meshes are typically extracted using automatic or semi-automatic methods [8,10,2]. Deep learning-based segmentation could further improve such methods [9], but widely adopted voxel-based segmentation methods do not meet the requirements of down-stream applications, i.e.…”

Section: Introductionmentioning

confidence: 99%

Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography

Wolterink

Leiner

Išgum

2019

Lecture Notes in Computer Science

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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.

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

Abstract: 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.

Cited by 24 publications

References 32 publications

A functionally personalized boundary condition model to improve estimates of fractional flow reserve with CT (CT‐FFR)

A functionally personalized boundary condition model to improve estimates of fractional flow reserve with CT (CT‐FFR)

Learning a Sparse Database for Patch-Based Medical Image Segmentation

Graph Convolutional Networks for Coronary Artery Segmentation in Cardiac CT Angiography

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