CoronARe ranks state-of-the-art methods in symbolic and tomographic coronary artery reconstruction from interventional C-arm rotational angiography. Specifically, we benchmark the performance of the methods using accurately pre-processed data, and study the effects of imperfect pre-processing conditions (segmentation and background subtraction errors). In this first iteration of the challenge, evaluation is performed in a controlled environment using digital phantom images, where accurate 3D ground truth is known.
CITATION STYLE
Çimen, S., Unberath, M., Frangi, A., & Maier, A. (2017). CoronARe: A coronary artery reconstruction challenge. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 10555 LNCS, pp. 96–104). Springer Verlag. https://doi.org/10.1007/978-3-319-67564-0_10
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