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Coronary Artery Segmentation and Skeletonization Based on Competing Fuzzy Connectedness Tree
Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Linköping University, Center for Medical Image Science and Visualization (CMIV).ORCID iD: 0000-0002-0442-3524
Linköping University, Center for Medical Image Science and Visualization (CMIV). Linköping University, Department of Medical and Health Sciences, Radiology. Linköping University, Faculty of Health Sciences. Östergötlands Läns Landsting, Centre for Medical Imaging, Department of Radiology in Linköping.ORCID iD: 0000-0002-7750-1917
2007 (English)In: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2007: 10th International Conference, Brisbane, Australia, October 29 - November 2, 2007, Proceedings, Part I / [ed] Nicholas Ayache, Sébastien Ourselin, Anthony Maeder, Springer Berlin/Heidelberg, 2007, Vol. 4791, 311-318 p.Conference paper, Published paper (Refereed)
Abstract [en]

We propose a new segmentation algorithm based on competing fuzzy connectedness theory, which is then used for visualizing coronary arteries in 3D CT angiography (CTA) images. The major difference compared to other fuzzy connectedness algorithms is that an additional data structure, the connectedness tree, is constructed at the same time as the seeds propagate. In preliminary evaluations, accurate result have been achieved with very limited user interaction. In addition to improving computational speed and segmentation results, the fuzzy connectedness tree algorithm also includes automated extraction of the vessel centerlines, which is a promising approach for creating curved plane reformat (CPR) images along arteries’ long axes.

Place, publisher, year, edition, pages
Springer Berlin/Heidelberg, 2007. Vol. 4791, 311-318 p.
Series
Lecture Notes in Computer Science, ISSN 0302-9743 (print), 1611-3349 (online) ; 4791
Keyword [en]
segmentation - fuzzy connectedness tree - centerline extraction - skeletonization - coronary artery - CT angiography
National Category
Radiology, Nuclear Medicine and Medical Imaging Computer Vision and Robotics (Autonomous Systems)
Identifiers
URN: urn:nbn:se:liu:diva-17816DOI: 10.1007/978-3-540-75757-3_38ISI: 000250916000038ISBN: 978-3-540-75756-6 (print)OAI: oai:DiVA.org:liu-17816DiVA: diva2:212251
Conference
10th International Conference on Medical Image Computing and Computer-Assisted Intervention, Brisbane, Australia, October 29 - November 2, 2007
Note

The original publication is available at www.springerlink.com: Chunliang Wang and Örjan Smedby, Coronary Artery Segmentation and Skeletonization Based on Competing Fuzzy Connectedness Tree, 2007, Medical Image Computing and Computer-Assisted Intervention, (4791), 311-318. http://dx.doi.org/10.1007/978-3-540-75757-3_38 Copyright: Springer-verlag http://www.springerlink.com/

Available from: 2009-04-21 Created: 2009-04-21 Last updated: 2014-09-24Bibliographically approved
In thesis
1. Computer Assisted Coronary CT Angiography Analysis: Disease-centered Software Development
Open this publication in new window or tab >>Computer Assisted Coronary CT Angiography Analysis: Disease-centered Software Development
2009 (English)Licentiate thesis, comprehensive summary (Other academic)
Abstract [en]

The substantial advances of coronary CTA have resulted in a boost of use of this new technique in the last several years, which brings a big challenge to radiologists by the increasing number of exams and the large amount of data for each patient. The main goal of this study was to develop a computer tool to facilitate coronary CTA analysis by combining knowledge of medicine and image processing.Firstly, a competing fuzzy connectedness tree algorithm was developed to segment the coronary arteries and extract centerlines for each branch. The new algorithm, which is an extension of the “virtual contrast injection” method, preserves the low density soft tissue around the coronary, which reduces the possibility of introducing false positive stenoses during segmentation.Secondly, this algorithm was implemented in open source software in which multiple visualization techniques were integrated into an intuitive user interface to facilitate user interaction and provide good over¬views of the processing results. Considerable efforts were put on optimizing the computa¬tional speed of the algorithm to meet the clinical requirements.Thirdly, an automatic seeding method, that can automatically remove rib cage and recognize the aortic root, was introduced into the interactive segmentation workflow to further minimize the requirement of user interactivity during post-processing. The automatic procedure is carried out right after the images are received, which saves users time after they open the data. Vessel enhance¬ment and quantitative 2D vessel contour analysis are also included in this new version of the software. In our preliminary experience, visually accurate segmentation results of major branches have been achieved in 74 cases (42 cases reported in paper II and 32 cases in paper III) using our software with limited user interaction. On 128 branches of 32 patients, the average overlap between the centerline created in our software and the manually created reference standard was 96.0%. The average distance between them was 0.38 mm, lower than the mean voxel size. The automatic procedure ran for 3-5 min as a single-thread application in the background. Interactive processing took 3 min in average with the latest version of software. In conclusion, the presented software provides fast and automatic coron¬ary artery segmentation and visualization. The accuracy of the centerline tracking was found to be acceptable when compared to manually created centerlines.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2009. 33 p.
Series
Linköping Studies in Health Sciences. Thesis, ISSN 1100-6013 ; 96
Keyword
Coronary CTA, vessel segmenation, centerline tracking, visualization
National Category
Radiology, Nuclear Medicine and Medical Imaging Computer Vision and Robotics (Autonomous Systems) Cardiac and Cardiovascular Systems
Identifiers
urn:nbn:se:liu:diva-17783 (URN)978-91-7393-643-9 (ISBN)
Presentation
2009-05-12, Wranne-salen, Campus US, Linköpings Universitet, Linköping, 13:00 (English)
Opponent
Supervisors
Available from: 2009-04-28 Created: 2009-04-20 Last updated: 2013-10-21Bibliographically approved
2. Computer-­Assisted  Coronary  CT  Angiography  Analysis: From  Software  Development  to  Clinical  Application
Open this publication in new window or tab >>Computer-­Assisted  Coronary  CT  Angiography  Analysis: From  Software  Development  to  Clinical  Application
2011 (English)Doctoral thesis, comprehensive summary (Other academic)
Abstract [en]

Advances in coronary Computed Tomography Angiography (CTA) have resulted in a boost in the use of this new technique in recent years, creating a challenge for radiologists due to the increasing number of exams and the large amount of data for each patient. The main goal of this study was to develop a computer tool to facilitate coronary CTA analysis by combining knowledge of medicine and image processing, and to evaluate the performance in clinical settings.

Firstly, a competing fuzzy connectedness tree algorithm was developed to segment the coronary arteries and extract centerlines for each branch. The new algorithm, which is an extension of the “virtual contrast injection” (VC) method, preserves the low-density soft tissue around the artery, and thus reduces the possibility of introducing false positive stenoses during segmentation. Visually reasonable results were obtained in clinical cases.

Secondly, this algorithm was implemented in open source software in which multiple visualization techniques were integrated into an intuitive user interface to facilitate user interaction and provide good over­views of the processing results. An automatic seeding method was introduced into the interactive segmentation workflow to eliminate the requirement of user initialization during post-processing. In 42 clinical cases, all main arteries and more than 85% of visible branches were identified, and testing the centerline extraction in a reference database gave results in good agreement with the gold standard.

Thirdly, the diagnostic accuracy of coronary CTA using the segmented 3D data from the VC method was evaluated on 30 clinical coronary CTA datasets and compared with the conventional reading method and a different 3D reading method, region growing (RG), from a commercial software. As a reference method, catheter angiography was used. The percentage of evaluable arteries, accuracy and negative predictive value (NPV) for detecting stenosis were, respectively, 86%, 74% and 93% for the conventional method, 83%, 71% and 92% for VC, and 64%, 56% and 93% for RG. Accuracy was significantly lower for the RG method than for the other two methods (p<0.01), whereas there was no significant difference in accuracy between the VC method and the conventional method (p = 0.22).

Furthermore, we developed a fast, level set-based algorithm for vessel segmentation, which is 10-20 times faster than the conventional methods without losing segmentation accuracy. It enables quantitative stenosis analysis at interactive speed.

In conclusion, the presented software provides fast and automatic coron­ary artery segmentation and visualization. The NPV of using only segmented 3D data is as good as using conventional 2D viewing techniques, which suggests a potential of using them as an initial step, with access to 2D reviewing techniques for suspected lesions and cases with heavy calcification. Combining the 3D visualization of segmentation data with the clinical workflow could shorten reading time.

Place, publisher, year, edition, pages
Linköping: Linköping University Electronic Press, 2011. 57 p.
Series
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1237
Keyword
Vessel segmentation, coronary CTA, fuzzy connectedness, level set, coronary artery disease
National Category
Computer Vision and Robotics (Autonomous Systems) Radiology, Nuclear Medicine and Medical Imaging
Identifiers
urn:nbn:se:liu:diva-68705 (URN)978-91-7393-191-5 (ISBN)
Public defence
2011-06-07, Wrannesalen, CMIV, Universitetssjukhuset, Campus US, Linköpings univeristet, Linköping, 09:00 (English)
Opponent
Supervisors
Available from: 2011-06-07 Created: 2011-05-30 Last updated: 2013-10-21Bibliographically approved

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