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- 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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| 3. |
- Wang, Chunliang, 1980-, et al.
(författare)
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Real-Time Interactive 3D Tumor Segmentation Using a Fast Level-Set Algorithm
- 2015
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Ingår i: Journal of Medical Imaging and Health Informatics. - : American Scientific Publishers. - 2156-7018 .- 2156-7026. ; 5:8, s. 1998-2002
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Tidskriftsartikel (refereegranskat)abstract
- A new level-set based interactive segmentation framework is introduced, where the algorithm learns the intensity distributions of the tumor and surrounding tissue from a line segment drawn by the user from the middle of the lesion towards the border. This information is used to design a likelihood function, which is then incorporated into the level-set framework as an external speed function guiding the segmentation. The endpoint of the input line segment sets a limit to the propagation of 3D region, i.e., when the zero-level-set crosses this point, the propagation is forced to stop. Finally, a fast level set algorithm with coherent propagation is used to solve the level set equation in real time. This allows the user to instantly see the 3D result while adjusting the position of the line segment to tune the parameters implicitly. The “fluctuating” character of the coherent propagation also enables the contour to coherently follow the mouse cursor’s motion when the user tries to fine-tune the position of the contour on the boundary, where the learned likelihood function may not necessarily change much. Preliminary results suggest that radiologists can easily learn how to use the proposed segmentation tool and perform relatively accurate segmentation with much less time than the conventional slice-by-slice based manual procedure.
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