Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography
[Display omitted] •Evaluation framework for coronary artery lumen segmentation and stenosis grading.•Description of the datasets and creation of reference standard is given.•Standardized evaluation measures are defined.•Results on current 11 submissions are presented.•Framework is open for new submi...
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| Vydané v: | Medical image analysis Ročník 17; číslo 8; s. 859 - 876 |
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| Hlavní autori: | , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , , |
| Médium: | Journal Article |
| Jazyk: | English |
| Vydavateľské údaje: |
Netherlands
Elsevier B.V
01.12.2013
Elsevier |
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| ISSN: | 1361-8415, 1361-8423, 1361-8423 |
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| Abstract | [Display omitted]
•Evaluation framework for coronary artery lumen segmentation and stenosis grading.•Description of the datasets and creation of reference standard is given.•Standardized evaluation measures are defined.•Results on current 11 submissions are presented.•Framework is open for new submissions.
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/. |
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| AbstractList | 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/. 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/.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/. 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 coronary.bigr.nl/stenoses/. Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CIA) 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 CIA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (I) (semi-)automatically detect and quantify stenosis on CIA, in comparison with quantitative coronary angiography (QCA) and CIA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CIA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CIA 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/. (C) 2013 Elsevier B.V. All rights reserved. [Display omitted] •Evaluation framework for coronary artery lumen segmentation and stenosis grading.•Description of the datasets and creation of reference standard is given.•Standardized evaluation measures are defined.•Results on current 11 submissions are presented.•Framework is open for new submissions. 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/. Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed tomography angiography (CIA) 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 CIA data. The objective of this evaluation framework is to demonstrate the feasibility of dedicated algorithms to: (I) (semi-)automatically detect and quantify stenosis on CIA, in comparison with quantitative coronary angiography (QCA) and CIA consensus reading, and (2) (semi-)automatically segment the coronary lumen on CIA, in comparison with expert's manual annotation. A database consisting of 48 multicenter multivendor cardiac CIA 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/. |
| Author | Eslami, A. Niessen, W.J. Moreno, R. Meijs, M.F.L. Orkisz, M. Flórez-Valencia, L. Mohr, B. Schaap, M. Wang, C. Vuçini, E. Metz, C.T. Dedic, A. Katouzian, A. Broersen, A. Ünay, D. Dharampal, A.S. Papadopoulou, S.L. de Graaf, M.A. Kirişli, H.A. Nieman, K. Unal, G. Najman, L. van Walsum, T. Cramer, M.J. Cetin, S. Öksüz, I. Krestin, G.P. Shahzad, R. Kitslaar, P.H. Masood, S. Chen, C.M. Meijboom, W.B. Melki, I. Precioso, F. van Vliet, L. Lor, K.L. Matuszewski, B. Goldenberg, R. |
| Author_xml | – sequence: 1 givenname: H.A. surname: Kirişli fullname: Kirişli, H.A. organization: Biomedical Imaging Group Rotterdam, Dept.of Radiology and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands – sequence: 2 givenname: M. surname: Schaap fullname: Schaap, M. organization: Biomedical Imaging Group Rotterdam, Dept.of Radiology and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands – sequence: 3 givenname: C.T. surname: Metz fullname: Metz, C.T. organization: Biomedical Imaging Group Rotterdam, Dept.of Radiology and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands – sequence: 4 givenname: A.S. surname: Dharampal fullname: Dharampal, A.S. organization: Dept.of Radiology, Erasmus MC, Rotterdam, The Netherlands – sequence: 5 givenname: W.B. surname: Meijboom fullname: Meijboom, W.B. organization: Dept.of Cardiology, Erasmus MC, Rotterdam, The Netherlands – sequence: 6 givenname: S.L. surname: Papadopoulou fullname: Papadopoulou, S.L. organization: Dept.of Radiology, Erasmus MC, Rotterdam, The Netherlands – sequence: 7 givenname: A. surname: Dedic fullname: Dedic, A. organization: Dept.of Cardiology, Erasmus MC, Rotterdam, The Netherlands – sequence: 8 givenname: K. surname: Nieman fullname: Nieman, K. organization: Dept.of Radiology, Erasmus MC, Rotterdam, The Netherlands – sequence: 9 givenname: M.A. surname: de Graaf fullname: de Graaf, M.A. organization: Dept.of Cardiology, Leiden UMC, Leiden, The Netherlands – sequence: 10 givenname: M.F.L. surname: Meijs fullname: Meijs, M.F.L. organization: Dept.of Cardiology, UMC Utrecht, Utrecht, The Netherlands – sequence: 11 givenname: M.J. surname: Cramer fullname: Cramer, M.J. organization: Dept.of Cardiology, UMC Utrecht, Utrecht, The Netherlands – sequence: 12 givenname: A. surname: Broersen fullname: Broersen, A. organization: Div. of Image Processing, Dept.of Radiology, Leiden UMC, Leiden, The Netherlands – sequence: 13 givenname: S. surname: Cetin fullname: Cetin, S. organization: Faculty of Engineering and Natural Sciences, SabancıUniversity, Turkey – sequence: 14 givenname: A. surname: Eslami fullname: Eslami, A. organization: Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany – sequence: 15 givenname: L. surname: Flórez-Valencia fullname: Flórez-Valencia, L. organization: Grupo Takina, Departamento de Ingeniería de Sistemas, Pontificia Universidad Javeriana, Bogotá, Colombia – sequence: 16 givenname: K.L. surname: Lor fullname: Lor, K.L. organization: Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan – sequence: 17 givenname: B. surname: Matuszewski fullname: Matuszewski, B. organization: School of Computing Engineering and Physical Sciences, University of Central Lancashire, Preston, UK – sequence: 18 givenname: I. surname: Melki fullname: Melki, I. organization: Université Paris-Est, Laboratoire d’Informatique Gaspard-Monge, Equipe A3SI, Noisy-le-Grand, France – sequence: 19 givenname: B. surname: Mohr fullname: Mohr, B. organization: Toshiba Medical Visualization Systems, Edinburgh, UK – sequence: 20 givenname: I. surname: Öksüz fullname: Öksüz, I. organization: Electrical and Electronics Engineering, Bahçeşehir University, Istanbul, Turkey – sequence: 21 givenname: R. surname: Shahzad fullname: Shahzad, R. organization: Biomedical Imaging Group Rotterdam, Dept.of Radiology and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands – sequence: 22 givenname: C. surname: Wang fullname: Wang, C. organization: Center for Medical Imaging Science and Visualization, Dept.of Medical and Health Sciences, Linköping University, Linköping, Sweden – sequence: 23 givenname: P.H. surname: Kitslaar fullname: Kitslaar, P.H. organization: Div. of Image Processing, Dept.of Radiology, Leiden UMC, Leiden, The Netherlands – sequence: 24 givenname: G. surname: Unal fullname: Unal, G. organization: Faculty of Engineering and Natural Sciences, SabancıUniversity, Turkey – sequence: 25 givenname: A. surname: Katouzian fullname: Katouzian, A. organization: Computer Aided Medical Procedures, Technical University of Munich, Munich, Germany – sequence: 26 givenname: M. surname: Orkisz fullname: Orkisz, M. organization: Université de Lyon, CREATIS, CNRS UMR 5220, INSERM U 1044, INSA-Lyon, Lyon, France – sequence: 27 givenname: C.M. surname: Chen fullname: Chen, C.M. organization: Institute of Biomedical Engineering, National Taiwan University, Taipei, Taiwan – sequence: 28 givenname: F. surname: Precioso fullname: Precioso, F. organization: University Nice-Sophia Antipolis, Laboratory of Informatics, Signal and Systems (I3S), Nice Sophia Antipolis, France – sequence: 29 givenname: L. surname: Najman fullname: Najman, L. organization: Université Paris-Est, Laboratoire d’Informatique Gaspard-Monge, Equipe A3SI, Noisy-le-Grand, France – sequence: 30 givenname: S. surname: Masood fullname: Masood, S. organization: Toshiba Medical Visualization Systems, Edinburgh, UK – sequence: 31 givenname: D. surname: Ünay fullname: Ünay, D. organization: Biomedical Engineering, Bahçeşehir University, Istanbul, Turkey – sequence: 32 givenname: L. surname: van Vliet fullname: van Vliet, L. organization: Quantitative Imaging Group, Imaging Science and Technology, Faculty of Applied Sciences, Delft Univ. of Technology, Delft, The Netherlands – sequence: 33 givenname: R. surname: Moreno fullname: Moreno, R. organization: Center for Medical Imaging Science and Visualization, Dept.of Medical and Health Sciences, Linköping University, Linköping, Sweden – sequence: 34 givenname: R. surname: Goldenberg fullname: Goldenberg, R. organization: Rcadia Medical Imaging, Haïfa, Israel – sequence: 35 givenname: E. surname: Vuçini fullname: Vuçini, E. organization: VRVis Research Center for Virtual Reality and Visualization, Vienna, Austria – sequence: 36 givenname: G.P. surname: Krestin fullname: Krestin, G.P. organization: Dept.of Radiology, Erasmus MC, Rotterdam, The Netherlands – sequence: 37 givenname: W.J. surname: Niessen fullname: Niessen, W.J. organization: Biomedical Imaging Group Rotterdam, Dept.of Radiology and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands – sequence: 38 givenname: T. surname: van Walsum fullname: van Walsum, T. email: coronarystenoses@bigr.nl organization: Biomedical Imaging Group Rotterdam, Dept.of Radiology and Med. Informatics, Erasmus MC, Rotterdam, The Netherlands |
| BackLink | https://www.ncbi.nlm.nih.gov/pubmed/23837963$$D View this record in MEDLINE/PubMed https://hal.science/hal-00874107$$DView record in HAL https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-258850$$DView record from Swedish Publication Index (Kungliga Tekniska Högskolan) https://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-95876$$DView record from Swedish Publication Index (Linköpings universitet) |
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| ContentType | Journal Article |
| Copyright | 2013 Elsevier B.V. Copyright © 2013 Elsevier B.V. All rights reserved. Distributed under a Creative Commons Attribution 4.0 International License |
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| DOI | 10.1016/j.media.2013.05.007 |
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| Issue | 8 |
| Keywords | stenoses quantification Computed tomography angiography (CTA) stenoses detection Standardized evaluation framework Coronary arteries |
| Language | English |
| License | Copyright © 2013 Elsevier B.V. All rights reserved. Distributed under a Creative Commons Attribution 4.0 International License: http://creativecommons.org/licenses/by/4.0 |
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•Evaluation framework for coronary artery lumen segmentation and stenosis grading.•Description of the datasets and creation of reference... Though conventional coronary angiography (CCA) has been the standard of reference for diagnosing coronary artery disease in the past decades, computed... |
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| SubjectTerms | Adult Aged Aged, 80 and over Algorithms Bioengineering Computed tomography angiography (CTA) Computer Science Coronary Angiography - standards Coronary arteries Coronary Stenosis - diagnostic imaging Engineering Sciences Humans Imaging Life Sciences Middle Aged Netherlands Pattern Recognition, Automated - methods Radiographic Image Enhancement - methods Radiographic Image Enhancement - standards Radiographic Image Interpretation, Computer-Assisted - methods Radiographic Image Interpretation, Computer-Assisted - standards Reproducibility of Results Sensitivity and Specificity Signal and Image processing Standardized evaluation framework stenoses detection stenoses quantification Tomography, X-Ray Computed - standards |
| Title | Standardized evaluation framework for evaluating coronary artery stenosis detection, stenosis quantification and lumen segmentation algorithms in computed tomography angiography |
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