| 1. |
- Medrano-Gracia, Pau, et al.
(författare)
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Construction of a coronary artery atlas from CT angiography
- 2014
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Ingår i: Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014. - Cham : Springer. ; , s. 513-520
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Konferensbidrag (refereegranskat)abstract
- Describing the detailed statistical anatomy of the coronary artery tree is important for determining the ætiology of heart disease. A number of studies have investigated geometrical features and have found that these correlate with clinical outcomes, e.g. bifurcation angle with major adverse cardiac events. These methodologies were mainly two-dimensional, manual and prone to inter-observer variability, and the data commonly relates to cases already with pathology. We propose a hybrid atlasing methodology to build a population of computational models of the coronary arteries to comprehensively and accurately assess anatomy including 3D size, geometry and shape descriptors. A random sample of 122 cardiac CT scans with a calcium score of zero was segmented and analysed using a standardised protocol. The resulting atlas includes, but is not limited to, the distributions of the coronary tree in terms of angles, diameters, centrelines, principal component shape analysis and cross-sectional contours. This novel resource will facilitate the improvement of stent design and provide a reference for hemodynamic simulations, and provides a basis for large normal and pathological databases.
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| 2. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Automatic multi-organ segmentation in non-enhanced CT datasets using hierarchical shape priors
- 2014
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Ingår i: 2014 22ND INTERNATIONAL CONFERENCE ON PATTERN RECOGNITION (ICPR). - : IEEE Computer Society. - 9781479952083 - 9781479952090 ; , s. 3327-3332
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Konferensbidrag (refereegranskat)abstract
- An automatic multi-organ segmentation method using hierarchical-shape-prior guided level sets is proposed. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, so that major structures with less population variety are at the top and smaller structures with higher irregularities are linked at a lower level. The segmentation is performed in a top-down fashion, where major structures are first segmented with higher confidence, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also provides extra cues to guide the segmentation of the lower-level structures. The proposed method was combined with a novel coherent propagating level set method, which is capable to detect local convergence and skip calculation in those parts, therefore significantly reducing computation time. Preliminary experiment results on a small number of clinical datasets are encouraging; the proposed method yielded a Dice coefficient above 90% for most major organs within a reasonable processing time without any user intervention.
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| 3. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Automatic multi–organ segmentation using fast model based level set method and hierarchical shape priors
- 2014
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Ingår i: Proceedings of the VISCERAL Organ Segmentation and Landmark Detection Challenge, co-located with IEEE International Symposium on Biomedical Imaging (ISBI 2014), Beijing, China, May 1, 2014. - : CEUR-WS. ; , s. 25-31
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Konferensbidrag (refereegranskat)abstract
- An automatic multi-organ segmentation pipeline is presented. The segmentation starts with stripping the body of skin and subcutaneous fat using threshold-based level-set methods. After registering the image to be processed against a standard subject picked from the training datasets, a series of model-based level set segmentation operations is carried out guided by hierarchical shape priors. The hierarchical shape priors are organized according to the anatomical hierarchy of the human body, starting with ventral cavity, and then divided into thoracic cavity and abdominopelvic cavity. The third level contains the individual organs such as liver, spleen and kidneys. The segmentation is performed in a top-down fashion, where major structures are segmented first, and their location information is then passed down to the lower level to initialize the segmentation, while boundary information from higher-level structures also constrains the segmentation of the lower-level structures. In our preliminary experiments, the proposed method yielded a Dice coeffcient around 90% for most major thoracic and abdominal organs in both contrastenhanced CT and non-enhanced datasets, while the average running time for segmenting ten organs was about 10 minutes.
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| 4. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Can segmented 3D images be used for stenosis evaluation in coronary CT angiography?
- 2012
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Ingår i: Acta Radiologica. - : Sage Publications. - 0284-1851 .- 1600-0455. ; 53:8, s. 845-851
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Tidskriftsartikel (refereegranskat)abstract
- Background: Thanks to the development of computed tomography (CT) scanners and computer software, accurate coronary artery segmentation can be achieved with minimum user interaction. However, the question remains whether we can use these segmented images for reliable diagnosis. Purpose: To retrospectively evaluate the diagnostic accuracy of coronary CT angiography (CCTA) using segmented 3D data for the detection of significant stenosis. Material and Methods: CCTA data-sets from 30 patients were acquired with a 64-slice CT scanner and segmented using the region growing (RG) method and the "virtual contrast injection" (VC) method. Three types of images of each patient were reviewed by different reviewers for the presence of stenosis with diameter reduction of 50% or more. The evaluation was performed on four main arteries of each patient (120 arteries in total). For the original series, the reviewer was allowed to use all the 2D and 3D visualization tools available (conventional method). For the segmented results from RG and VC, only maximum intensity projection was used. Evaluation results were compared with catheter angiography (CA) for each artery in a blinded fashion. Results: Overall, 34 arteries with significant stenosis were identified by CA. The percentage of evaluable arteries, accuracy and negative predictive value 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). Conclusion: The diagnostic accuracy for the RG-segmented 3D data is lower than those with access to 2D images, whereas the VC method shows diagnostic accuracy similar to the conventional method.
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| 5. |
- Wang, Chunliang, 1980-
(författare)
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Computer-Assisted Coronary CT Angiography Analysis : From Software Development to Clinical Application
- 2011
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- 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.
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| 6. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Fast level-set based image segmentation using coherent propagation
- 2014
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Ingår i: Medical physics (Lancaster). - : John Wiley and Sons Ltd. - 0094-2405. ; 41:7
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: The level-set method is known to require long computation time for 3D image segmentation, which limits its usage in clinical workflow. The goal of this study was to develop a fast level-set algorithm based on the coherent propagation method and explore its character using clinical datasets. Methods: The coherent propagation algorithm allows level set functions to converge faster by forcing the contour to move monotonically according to a predicted developing trend. Repeated temporary backwards propagation, caused by noise or numerical errors, is then avoided. It also makes it possible to detect local convergence, so that the parts of the boundary that have reached their final position can be excluded in subsequent iterations, thus reducing computation time. To compensate for the overshoot error, forward and backward coherent propagation is repeated periodically. This can result in fluctuations of great magnitude in parts of the contour. In this paper, a new gradual convergence scheme using a damping factor is proposed to address this problem. The new algorithm is also generalized to non-narrow band cases. Finally, the coherent propagation approach is combined with a new distance-regularized level set, which eliminates the needs of reinitialization of the distance. Results: Compared with the sparse field method implemented in the widely available ITKSnap software, the proposed algorithm is about 10 times faster when used for brain segmentation and about 100 times faster for aorta segmentation. Using a multiresolution approach, the new method achieved 50 times speed-up in liver segmentation. The Dice coefficient between the proposed method and the sparse field method is above 99% in most cases. Conclusions: A generalized coherent propagation algorithm for level set evolution yielded substantial improvement in processing time with both synthetic datasets and medical images.
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| 7. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Fully automatic brain segmentation using model-guided level sets and skeleton-based models
- 2013
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Konferensbidrag (refereegranskat)abstract
- A fully automatic brain segmentation method is presented. First the skull is stripped using a model-based level set on T1-weighted inversion recovery images, then the brain ventricles and basal ganglia are segmented using the same method on T1-weighted images. The central white matter is segmented using a regular level set method but with high curvature regulation. To segment the cortical gray matter, a skeleton-based model is created by extracting the mid-surface of the gray matter from a preliminary segmentation using a threshold-based level set. An implicit model is then built by defining the thickness of the gray matter to be 2.7 mm. This model is incorporated into the level set framework and used to guide a second round more precise segmentation. Preliminary experiments show that the proposed method can provide relatively accurate results compared with the segmentation done by human observers. The processing time is considerably shorter than most conventional automatic brain segmentation methods.
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| 8. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Integrating automatic and interactive method for coronary artery segmentation : let PACS workstation think ahead
- 2010
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Ingår i: International Journal of Computer Assisted Radiology and Surgery. - : Springer Science and Business Media LLC. - 1861-6410 .- 1861-6429. ; 5:3, s. 275-285
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Tidskriftsartikel (refereegranskat)abstract
- Purpose: To provide an efficient method to extract useful information from the increasing amount of coronary CTA.Methods: A quantitative coronary CTA analysis tool was built on OsiriX, which integrates both fully automatic and interactive methods for coronary artery extraction. The computational power of an ordinary PC is exploited by running the non-supervised coronary artery segmentation and centerline tracking in the background as soon as the images are received. When the user opens the data, the software provides a real-time interactive analysis environment.Results: The average overlap between the centerline created in our software and the reference standard was 96.0%. The average distance between them was 0.38 mm. The automatic procedure runs for 3-5 min as a single-thread application in background. Interactive processing takes 3 min in average.Conclusion: In preliminary experiments, the software achieved higher efficiency than the former interactive method, and reasonable accuracy compared to manual vessel extraction.
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