Literature DB >> 23837963

Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography.

H A Kirişli1, M Schaap, C T Metz, A S Dharampal, W B Meijboom, S L Papadopoulou, A Dedic, K Nieman, M A de Graaf, M F L Meijs, M J Cramer, A Broersen, S Cetin, A Eslami, L Flórez-Valencia, K L Lor, B Matuszewski, I Melki, B Mohr, I Oksüz, R Shahzad, C Wang, P H Kitslaar, G Unal, A Katouzian, M Örkisz, C M Chen, F Precioso, L Najman, S Masood, D Ünay, L van Vliet, R Moreno, R Goldenberg, E Vuçini, G P Krestin, W J Niessen, T van Walsum.   

Abstract

Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CTA) has rapidly emerged, and is nowadays widely used in clinical practice. Here, we introduce a standardized evaluation framework to reliably evaluate and compare the performance of the algorithms devised to detect and quantify the coronary artery stenoses, and to segment the coronary artery lumen in CTA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (1) (semi-)automatically detect and quantify stenosis on CTA, in comparison with quantitative coronary angiography (QCA) and CTA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CTA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CTA datasets with corresponding reference standards are described and made available. The algorithms from 11 research groups were quantitatively evaluated and compared. The results show that (1) some of the current stenosis detection/quantification algorithms may be used for triage or as a second-reader in clinical practice, and that (2) automatic lumen segmentation is possible with a precision similar to that obtained by experts. The framework is open for new submissions through the website, at http://coronary.bigr.nl/stenoses/.
Copyright © 2013 Elsevier B.V. All rights reserved.

Entities:  

Keywords:  Computed tomography angiography (CTA); Coronary arteries; Standardized evaluation framework; stenoses detection; stenoses quantification

Mesh:

Year:  2013        PMID: 23837963     DOI: 10.1016/j.media.2013.05.007

Source DB:  PubMed          Journal:  Med Image Anal        ISSN: 1361-8415            Impact factor:   8.545


  25 in total

1.  Automatic segmentation, detection and quantification of coronary artery stenoses on CTA.

Authors:  Rahil Shahzad; Hortense Kirişli; Coert Metz; Hui Tang; Michiel Schaap; Lucas van Vliet; Wiro Niessen; Theo van Walsum
Journal:  Int J Cardiovasc Imaging       Date:  2013-08-08       Impact factor: 2.357

2.  Structured learning algorithm for detection of nonobstructive and obstructive coronary plaque lesions from computed tomography angiography.

Authors:  Dongwoo Kang; Damini Dey; Piotr J Slomka; Reza Arsanjani; Ryo Nakazato; Hyunsuk Ko; Daniel S Berman; Debiao Li; C-C Jay Kuo
Journal:  J Med Imaging (Bellingham)       Date:  2015-03-06

3.  Inferior vena cava segmentation with parameter propagation and graph cut.

Authors:  Zixu Yan; Feng Chen; Fa Wu; Dexing Kong
Journal:  Int J Comput Assist Radiol Surg       Date:  2017-04-18       Impact factor: 2.924

4.  Automatic multiorgan segmentation in CT images of the male pelvis using region-specific hierarchical appearance cluster models.

Authors:  Dengwang Li; Pengxiao Zang; Xiangfei Chai; Yi Cui; Ruijiang Li; Lei Xing
Journal:  Med Phys       Date:  2016-10       Impact factor: 4.071

Review 5.  Cardiac imaging: working towards fully-automated machine analysis & interpretation.

Authors:  Piotr J Slomka; Damini Dey; Arkadiusz Sitek; Manish Motwani; Daniel S Berman; Guido Germano
Journal:  Expert Rev Med Devices       Date:  2017-03       Impact factor: 3.166

Review 6.  Extraction of Coronary Atherosclerotic Plaques From Computed Tomography Imaging: A Review of Recent Methods.

Authors:  Haipeng Liu; Aleksandra Wingert; Jian'an Wang; Jucheng Zhang; Xinhong Wang; Jianzhong Sun; Fei Chen; Syed Ghufran Khalid; Jun Jiang; Dingchang Zheng
Journal:  Front Cardiovasc Med       Date:  2021-02-10

7.  Image-based assessment of uncertainty in quantification of carotid lumen.

Authors:  Lilli Kaufhold; Andreas Harloff; Christian Schumann; Axel J Krafft; Juergen Hennig; Anja Hennemuth
Journal:  J Med Imaging (Bellingham)       Date:  2018-09-24

8.  Optimal Joint Detection and Estimation That Maximizes ROC-Type Curves.

Authors:  Adam Wunderlich; Bart Goossens; Craig K Abbey
Journal:  IEEE Trans Med Imaging       Date:  2016-04-13       Impact factor: 10.048

9.  Algorithms for segmenting cerebral time-of-flight magnetic resonance angiograms from volunteers and anemic patients.

Authors:  Alexander Saunders; Kevin S King; Stefan Blüml; John C Wood; Matthew Borzage
Journal:  J Med Imaging (Bellingham)       Date:  2021-04-28

10.  3D multimodal cardiac data reconstruction using angiography and computerized tomographic angiography registration.

Authors:  Rohollah Moosavi Tayebi; Rahmita Wirza; Puteri S B Sulaiman; Mohd Zamrin Dimon; Fatimah Khalid; Aqeel Al-Surmi; Samaneh Mazaheri
Journal:  J Cardiothorac Surg       Date:  2015-04-22       Impact factor: 1.637

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