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Proceedings Paper

Semi-automatic aortic valve tract segmentation in 3D cardiac magnetic resonance images using shape-based B-spline explicit active surfaces
Author(s): Sandro Queirós; Pedro Morais; Jaime C. Fonseca; Jan D'hooge; João L. Vilaça
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Paper Abstract

Accurate preoperative sizing of the aortic annulus (AoA) is crucial to determine the best fitting prosthesis to be implanted during transcatheter aortic valve (AV) implantation (TAVI). Although multidetector row computed tomography is currently the standard imaging modality for such assessment, 3D cardiac magnetic resonance (CMR) is a feasible radiation-free alternative. However, automatic AV segmentation and sizing in 3D CMR images is so far underexplored. In this sense, this study proposes a novel semi-automatic algorithm for AV tract segmentation and sizing in 3D CMR images using the recently presented shape-based B-spline Explicit Active Surfaces (BEAS) framework. Upon initializing the AV tract surface using two user-defined points, a dual-stage shape-based BEAS evolution is performed to segment the patient-specific AV wall. The obtained surface is then aligned with multiple reference AV tract surfaces to estimate the location of the aortic annulus, allowing to extract the relevant clinical measurements. The framework was validated in thirty datasets from a publicly available CMR benchmark, assessing the segmentation accuracy and the measurements’ agreement against manual sizing. The automated segmentation showed an average absolute distance error of 0.54 mm against manually delineated surfaces, while demonstrating to be robust against the algorithm’s parameters. In its turn, automated AoA area-derived diameters showed an excellent agreement against manual-based ones (-0.30±0.77 mm), being comparable to the interobserver agreement. Overall, the proposed framework proved to be accurate, robust and computationally efficient (around 1 sec) for AV tract segmentation and sizing in 3D CMR images, thus showing its potential for preoperative TAVI planning.

Paper Details

Date Published: 15 March 2019
PDF: 8 pages
Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 1094918 (15 March 2019); doi: 10.1117/12.2512777
Show Author Affiliations
Sandro Queirós, Life and Health Sciences Research Institute (Portugal)
KU Leuven (Belgium)
Univ. do Minho (Portugal)
Pedro Morais, 2Ai - Polytechnic Institute of Cávado (Portugal)
Jaime C. Fonseca, Univ. do Minho (Portugal)
Jan D'hooge, KU Leuven (Belgium)
João L. Vilaça, 2Ai Instituto Politécnico do Cávado e do Ave (Portugal)


Published in SPIE Proceedings Vol. 10949:
Medical Imaging 2019: Image Processing
Elsa D. Angelini; Bennett A. Landman, Editor(s)

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