Standardized Evaluation Methodology and Reference Database for Evaluating Coronary Artery Centerline Extraction Algorithms

By Michiel Schaap, C. Metz, T. Walsum, A. G. Giessen, A. Weustink, N. Mollet, Christian Bauer, H. Bogunovic, Carlos Castro-Gonzalez, Xiang Deng and 20 more. Medical Image Anal., 2009

406 citations recorded by the source corpus.

Efficiently obtaining a reliable coronary artery centerline from computed tomography angiography data is relevant in clinical practice. Whereas numerous methods have been presented for this purpose, up to now no standardized evaluation methodology has been published to reliably evaluate and compare the performance of the existing or newly developed coronary artery centerline extraction algorithms. This paper describes a standardized evaluation methodology and reference database for the quantitative evaluation of coronary artery centerline extraction algorithms. The contribution of this work is fourfold: 1) a method is described to create a consensus centerline with multiple observers, 2) well-defined measures are presented for the evaluation of coronary artery centerline extraction algorithms, 3) a database containing thirty-two cardiac CTA datasets with corresponding reference standard is described and made available, and 4) thirteen coronary artery centerline extraction algorithms, implemented by different research groups, are quantitatively evaluated and compared. The presented evaluation framework is made available to the medical imaging community for benchmarking existing or newly developed coronary centerline extraction algorithms.

Subfields: Pulmonary and Respiratory Medicine, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/695de22afbf5acb9f9e547b03fd42669d4bde544

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In Pulmonary and Respiratory Medicine

In Radiology, Nuclear Medicine and Imaging

Citations in the corpus

The Multimodal Brain Tumor Image Segmentation Benchmark (BRATS) (2015, background): “…examples of community benchmarks dealing with medical image segmentation and annotation include algorithms for artery centerline extraction [61], [62], vessel segmentation and stenosis grading [63], liver segmentation [64], [65], detection of microaneurysms in digital color fundus photographs…”
Dynamic Snake Convolution based on Topological Geometric Constraints for Tubular Structure Segmentation (2023, methodology): “Meanwhile, to objectively verify the continuity of the coronary artery segmentation, the overlap until first error (OF) [23] is used to evaluate the completeness of the extracted centerline.”
Deep Learning for Cardiac Image Segmentation: A Review (2019, background): “, 2019) 2017 CT/MRI 60/60 WHS myocardium infarction, atrial fibrillation, tricuspid regurgitation, aortic valve stenosis, Alagille syndrome, Williams syndrome, dilated cardiomyopathy, aortic coarctation, Tetralogy of Fallot CAT08 (Schaap et al., 2009) 2008 CTA 32 Coronary artery centerline Patients ”
Computer Methods and Programs in Biomedicine (neutral): “…[142] 100 CFP VICAVR 58 CFP ( http://www.varpa.es ) VAMPIRE 8 FA ( http://vampire.computing . dundee.ac.uk) CASDQEF [143] Coronary 48 CTA ROTTERDAM [144] 20 CTA VESSEL12 [145] Lung 20 CT 3D-IRCADb Liver 22 CT ( http://www.ircad.fr/research/3dircadb/ ) OSMSC Cardiovascular and 93 MRA and CT (…”
Longitudinal multiple sclerosis lesion segmentation: Resource and challenge (2017, positive): “In recent years there has been a shift in the community to launch these data sets as a challenge associated with a workshop or conference (Styner et al., 2008; Schaap et al., 2009; Heimann et al., 2009; Menze et al., 2015; Mendrik et al., 2015; Maier et al., 2017).”
elastix: A Toolbox for Intensity-Based Medical Image Registration (2010, background): “Open challenges, such as recently organized for liver segmentation [65] and coronary artery centerline extraction [66], would be an interesting way to compare image”
Digital Topology and Geometry in Medical Imaging: A Survey (2015, methodology): “The Rotterdam coronary axis tracking evaluation group [330] reported results of a comprehensive evaluation study for centerline detection algorithms for coronary vasculature using consensus centerlines by multiple observers.”