Automatic detection of carotid arteries in computed tomography angiography: a proof of concept protocol
Atherosclerosis is one of the leading causes of mortality in the western world. Computed tomography angiography (CTA) is the conventional imaging method used for pre-surgery assessment of the blood flow within the carotid vessel. In this paper, we present a proof of concept of a novel, fast and oper...
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Published in | International Journal of Cardiovascular Imaging Vol. 32; no. 8; pp. 1299 - 1310 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
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Dordrecht
Springer Netherlands
01.08.2016
Springer Nature B.V |
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Abstract | Atherosclerosis is one of the leading causes of mortality in the western world. Computed tomography angiography (CTA) is the conventional imaging method used for pre-surgery assessment of the blood flow within the carotid vessel. In this paper, we present a proof of concept of a novel, fast and operator independent protocol for the automatic detection (seeding) of the carotid arteries in CTA in the thorax and upper neck region. The dataset is composed of 14 patients’ CTA images of the neck region. The performance of this method is compared with manual seeding by four trained operators. Inter-operator variation is also assessed based on the dataset. The minimum, average and maximum coefficient of variation among the operators was (0, 2, 5 %), respectively. The performance of our method is comparable with the state of the art alternative, presenting a detection rate of 75 and 71 % for the lowest and uppermost image levels, respectively. The mean processing time is 167 s per patient versus 386 s for manual seeding. There are no significant differences between the manual and automatic seed positions in the volumes (
p
= 0.29). A fast, operator independent protocol was developed for the automatic detection of carotid arteries in CTA. The results are encouraging and provide the basis for the creation of automatic detection and analysis tools for carotid arteries. |
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AbstractList | Atherosclerosis is one of the leading causes of mortality in the western world. Computed tomography angiography (CTA) is the conventional imaging method used for pre-surgery assessment of the blood flow within the carotid vessel. In this paper, we present a proof of concept of a novel, fast and operator independent protocol for the automatic detection (seeding) of the carotid arteries in CTA in the thorax and upper neck region. The dataset is composed of 14 patients' CTA images of the neck region. The performance of this method is compared with manual seeding by four trained operators. Inter-operator variation is also assessed based on the dataset. The minimum, average and maximum coefficient of variation among the operators was (0, 2, 5 %), respectively. The performance of our method is comparable with the state of the art alternative, presenting a detection rate of 75 and 71 % for the lowest and uppermost image levels, respectively. The mean processing time is 167 s per patient versus 386 s for manual seeding. There are no significant differences between the manual and automatic seed positions in the volumes (p = 0.29). A fast, operator independent protocol was developed for the automatic detection of carotid arteries in CTA. The results are encouraging and provide the basis for the creation of automatic detection and analysis tools for carotid arteries. Atherosclerosis is one of the leading causes of mortality in the western world. Computed tomography angiography (CTA) is the conventional imaging method used for pre-surgery assessment of the blood flow within the carotid vessel. In this paper, we present a proof of concept of a novel, fast and operator independent protocol for the automatic detection (seeding) of the carotid arteries in CTA in the thorax and upper neck region. The dataset is composed of 14 patients' CTA images of the neck region. The performance of this method is compared with manual seeding by four trained operators. Inter-operator variation is also assessed based on the dataset. The minimum, average and maximum coefficient of variation among the operators was (0, 2, 5%), respectively. The performance of our method is comparable with the state of the art alternative, presenting a detection rate of 75 and 71% for the lowest and uppermost image levels, respectively. The mean processing time is 167 s per patient versus 386 s for manual seeding. There are no significant differences between the manual and automatic seed positions in the volumes (p=0.29). A fast, operator independent protocol was developed for the automatic detection of carotid arteries in CTA. The results are encouraging and provide the basis for the creation of automatic detection and analysis tools for carotid arteries. Atherosclerosis is one of the leading causes of mortality in the western world. Computed tomography angiography (CTA) is the conventional imaging method used for pre-surgery assessment of the blood flow within the carotid vessel. In this paper, we present a proof of concept of a novel, fast and operator independent protocol for the automatic detection (seeding) of the carotid arteries in CTA in the thorax and upper neck region. The dataset is composed of 14 patients’ CTA images of the neck region. The performance of this method is compared with manual seeding by four trained operators. Inter-operator variation is also assessed based on the dataset. The minimum, average and maximum coefficient of variation among the operators was (0, 2, 5 %), respectively. The performance of our method is comparable with the state of the art alternative, presenting a detection rate of 75 and 71 % for the lowest and uppermost image levels, respectively. The mean processing time is 167 s per patient versus 386 s for manual seeding. There are no significant differences between the manual and automatic seed positions in the volumes ( p = 0.29). A fast, operator independent protocol was developed for the automatic detection of carotid arteries in CTA. The results are encouraging and provide the basis for the creation of automatic detection and analysis tools for carotid arteries. |
Author | dos Santos, Florentino Luciano Caetano Salenius, Juha Joutsen, Atte Eskola, Hannu Paci, Michelangelo |
Author_xml | – sequence: 1 givenname: Florentino Luciano Caetano orcidid: 0000-0001-8151-3585 surname: dos Santos fullname: dos Santos, Florentino Luciano Caetano email: florentino.caetanodossantos@tut.fi organization: Department of Electronics and Communications Engineering, Tampere University of Technology – sequence: 2 givenname: Atte surname: Joutsen fullname: Joutsen, Atte organization: Department of Electronics and Communications Engineering, Tampere University of Technology – sequence: 3 givenname: Michelangelo surname: Paci fullname: Paci, Michelangelo organization: Department of Electronics and Communications Engineering, Tampere University of Technology, BioMediTech – sequence: 4 givenname: Juha surname: Salenius fullname: Salenius, Juha organization: Division of Vascular Surgery, Department of Surgery, Tampere University Hospital and Medical School – sequence: 5 givenname: Hannu surname: Eskola fullname: Eskola, Hannu organization: Department of Electronics and Communications Engineering, Tampere University of Technology, Department of Radiology, Regional Imaging Centre, Tampere University Hospital |
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Cites_doi | 10.1111/j.2042-7158.1994.tb03745.x 10.1161/CIR.0b013e31828124ad 10.1007/s00330-005-2801-2 10.1148/radiology.186.2.8421726 10.5551/jat.21279 10.1016/j.cmpb.2013.10.012 10.1007/11566489 10.1016/j.ejvs.2008.04.016 10.2478/amcs-2014-0003 10.1007/11566489_104 10.1109/IEMBS.2010.5626473 10.1007/s10554-008-9309-1 10.1152/ajpheart.00989.2006 10.1007/s10334-003-0030-8 10.1109/ISBI.2008.4540923 10.1016/S0262-8856(98)00160-7 10.1016/0262-8856(90)90059-E 10.1161/01.STR.30.9.1751 10.1053/j.tvir.2007.03.003 10.2214/ajr.146.2.233 10.1109/TMI.2009.2025702 10.1109/IEMBS.2008.4649871 10.1177/0954411913480622 10.3174/ajnr.A3659 |
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