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Level-set based vessel segmentation accelerated with periodic monotonic speed function
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
Uppsala University.
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). Östergötlands Läns Landsting, Center for Diagnostics, Department of Radiology in Linköping.ORCID iD: 0000-0002-7750-1917
2011 (English)In: Medical Imaging 2011: Image Processing / [ed] Benoit M. Dawant, David R. Haynor, SPIE - International Society for Optical Engineering, 2011, Vol. 7962, 79621M-1-79621M-7 p.Conference paper (Refereed)
Abstract [en]

To accelerate level-set based abdominal aorta segmentation on CTA data, we propose a periodic monotonic speed function, which allows segments of the contour to expand within one period and to shrink in the next period, i.e., coherent propagation. This strategy avoids the contour’s local wiggling behavior which often occurs during the propagating when certain points move faster than the neighbors, as the curvature force will move them backwards even though the whole neighborhood will eventually move forwards. Using coherent propagation, these faster points will, instead, stay in their places waiting for their neighbors to catch up. A period ends when all the expanding/shrinking segments can no longer expand/shrink, which means that they have reached the border of the vessel or stopped by the curvature force. Coherent propagation also allows us to implement a modified narrow band level set algorithm that prevents the endless computation in points that have reached the vessel border. As these points’ expanding/shrinking trend changes just after several iterations, the computation in the remaining iterations of one period can focus on the actually growing parts. Finally, a new convergence detection method is used to permanently stop updating the local level set function when the 0-level set is stationary in a voxel for several periods. The segmentation stops naturally when all points on the contour are stationary. In our preliminary experiments, significant speedup (about 10 times) was achieved on 3D data with almost no loss of segmentation accuracy.

Place, publisher, year, edition, pages
SPIE - International Society for Optical Engineering, 2011. Vol. 7962, 79621M-1-79621M-7 p.
Progress in Biomedical Optics and Imaging, ISSN 1605-7422 ; Vol. 7962
Keyword [en]
Level-set; image segmentation; monotonic speed function; coherent propagation; narrow band; sparse field
National Category
Medical Image Processing
URN: urn:nbn:se:liu:diva-68006DOI: 10.1117/12.876704ISI: 000294154900056ISBN: 9780819485045OAI: diva2:414942
Medical imaging 2011 - Image Processing, Lake Buena Vista, Florida, USA, 14–16 February 2011

Original Publication: Chunliang Wang, Hans Frimmel and Örjan Smedby, Level-set based vessel segmentation accelerated with periodic monotonic speed function, 2011, SPIE medical imaging 2011 Lake Buena Vista, Florida, USA. Copyright 2011 Society of Photo-Optical Instrumentation Engineers. One print or electronic copy may be made for personal use only. Systematic reproduction and distribution, duplication of any material in this paper for a fee or for commercial purposes, or modification of the content of the paper are prohibited.

Available from: 2011-05-05 Created: 2011-05-05 Last updated: 2015-08-20Bibliographically approved
In thesis
1. 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.
Linköping University Medical Dissertations, ISSN 0345-0082 ; 1237
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
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)
Available from: 2011-06-07 Created: 2011-05-30 Last updated: 2013-10-21Bibliographically approved

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