The Experts below are selected from a list of 44502 Experts worldwide ranked by ideXlab platform
Stephan Haulon - One of the best experts on this subject based on the ideXlab platform.
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Centerline is not as accurate as outer curvature length to estimate thoracic endograft length
European Journal of Vascular and Endovascular Surgery, 2013Co-Authors: Adrien Kaladji, R Spear, Adrien Hertault, Jonathan Sobocinski, Blandine Maurel, Stephan HaulonAbstract:Background To assess the accuracy of the aortic outer curvature length for thoracic endograft planning. Methods Seventy-four patients (58 men, 66.4 ± 14 years) who underwent thoracic endovascular aortic repair between 2009 and 2011 treated with a Cook Medical endograft were enrolled in this retrospective study. Immediate postoperative CT scans were analysed using EndoSize software. Three vessel lengths were computed between two fixed landmarks placed at each end of the endograft: the straightline (axial) length, the Centerline length and the outer curvature length. A tortuosity index was defined as the ratio of the Centerline length/straightline length. A Student t test and a Pearson correlation coefficient were used to examine the results. Results We found a significant difference between the Centerline length (135.4 ± 24 mm) and that of the endograft (160 ± 29 mm) ( p r = .818, p r = .587, p r = .53, p p = .792). Conclusion The outer curvature length more accurately reflects that of the deployed endograft and may prove more accurate than Centerlines in planning thoracic endografts.
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Centerline is not as accurate as outer curvature length to estimate thoracic endograft length.
European Journal of Vascular and Endovascular Surgery, 2013Co-Authors: Adrien Kaladji, R Spear, Adrien Hertault, Jonathan Sobocinski, Blandine Maurel, Stephan HaulonAbstract:BACKGROUND: To assess the accuracy of the aortic outer curvature length for thoracic endograft planning. METHODS: Seventy-four patients (58 men, 66.4 ± 14 years) who underwent thoracic endovascular aortic repair between 2009 and 2011 treated with a Cook Medical endograft were enrolled in this retrospective study. Immediate postoperative CT scans were analysed using EndoSize software. Three vessel lengths were computed between two fixed landmarks placed at each end of the endograft: the straightline (axial) length, the Centerline length and the outer curvature length. A tortuosity index was defined as the ratio of the Centerline length/straightline length. A Student t test and a Pearson correlation coefficient were used to examine the results. RESULTS: We found a significant difference between the Centerline length (135.4 ± 24 mm) and that of the endograft (160 ± 29 mm) (p < .0001). This difference correlates with the tortuosity index (r = .818, p < .0001), the endograft length (r = .587, p < .0001), and the diameter of the endograft (r = .53, p < .0001). However, the outer curvature length (161.3 ± 29 mm) and the endograft length (160 ± 29 mm) were similar (p = .792). CONCLUSION: The outer curvature length more accurately reflects that of the deployed endograft and may prove more accurate than Centerlines in planning thoracic endografts.
Wiro J Niessen - One of the best experts on this subject based on the ideXlab platform.
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cardiac motion corrected iterative cone beam ct reconstruction using a semi automatic minimum cost path based coronary Centerline extraction
Computerized Medical Imaging and Graphics, 2012Co-Authors: Coert Metz, Michiel Schaap, Wiro J Niessen, Stefan Klein, Alfonso Agatino Isola, Michael GrassAbstract:In this paper a method which combines iterative computed tomography reconstruction and coronary Centerline extraction technique to obtain motion artifact-free reconstructed images of the coronary arteries are proposed and evaluated. The method relies on motion-vector fields derived from a set of coronary Centerlines extracted at multiple cardiac phases within the R-R interval. Hereto, start and end points are provided by the user in one time-frame only. Using an elastic image registration, these points are propagated to all the remaining cardiac phases. Consequently, a multi-phase three-dimensional coronary Centerline is determined by applying a semi-automatic minimum cost path based extraction method. Corresponding Centerline positions are used to determine the relative motion-vector fields from phase to phase. Finally, dense motion-vector fields are achieved by thin-plate-spline interpolation and used to perform a motion-corrected iterative reconstruction of a selected region of interest. The performance of the method is validated on five patients, showing the improved sharpness of cardiac motion-corrected gated iterative reconstructions compared to the results achieved by a classical gated iterative method. The results are also compared to known manual and fully automatic coronary artery motion estimation methods.
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coronary Centerline extraction from ct coronary angiography images using a minimum cost path approach
Medical Physics, 2009Co-Authors: Coert Metz, Michiel Schaap, T Van Walsum, Annick C. Weustink, Nico R Mollet, Wiro J NiessenAbstract:Purpose: The application and large-scale evaluation of minimum cost path approaches for coronary Centerline extraction from computed tomography coronary angiography (CTCA) data and the development and evaluation of a novel method to reduce the user-interaction time. Methods: A semiautomatic method based on a minimum cost path approach is evaluated for two different cost functions. The first cost function is based on a frequently used vesselness measure and intensity information, and the second is a recently proposed cost function based on region statistics. User interaction is minimized to one or two mouse clicks distally in the coronary artery. The starting point for the minimum cost path search is automatically determined using a newly developed method that finds a point in the center of the aorta in one of the axial slices. This step ensures that all computationally expensive parts of the algorithm can be precomputed. Results: The performance of the aorta localization procedure was demonstrated by a success rate of 100% in 75 images. The success rate and accuracy of Centerline extraction was quantitatively evaluated on 48 coronary arteries in 12 images by comparing extracted Centerlines with a manually annotated reference standard. The method was able to extract 88% and 47% of the vessel Centerlines correctly using the vesselness/intensity and region statistics cost function, respectively. For only the proximal part of the vessels these values were 97% and 86%, respectively. Accuracy of Centerline extraction, defined as the average distance from correctly automatically extracted parts of the Centerline to the reference standard, was 0.64 mm for the vesselness/intensity and 0.51 mm for the region statistics cost function. The interobserver variability was 99% for the success rate measure and 0.42 mm for the accuracy measure. Qualitative evaluation using the best performing cost function resulted in successful Centerline extraction for 233 out of the 252 coronaries (92%) in 63 additional CTCA images. Conclusions: The presented results, in combination with minimal user interaction and low computation time, show that minimum cost path approaches can effectively be applied as a preprocessing step for subsequent analysis in clinical practice and biomedical research.
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On the Evaluation of Coronary Artery Centerline Extraction Algorithms
IFMBE Proceedings, 2009Co-Authors: Michiel Schaap, Coert Metz, A G Van Der Giessen, T Van Walsum, Annick C. Weustink, N. Mollet, Gabriel P. Krestin, Wiro J NiessenAbstract:The extraction of coronary artery Centerlines from computed tomography angiography data is relevant in clinical practice. A large number of (semi-)automatic methods have therefore been presented for this purpose. However, prior to the work described in this paper no standardized evaluation methodology has been published to reliably evaluate and compare the performance of coronary artery Centerline extraction algorithms. This paper describes the deployment of a publicly available database of coronary CTA data with corresponding reference standard derived from manually annotated Centerlines, a standardized evaluation framework consisting of well-defined evaluation measures, and an on-line tool for the comparison of coronary CTA Centerline extraction techniques.
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semi automatic coronary artery Centerline extraction in computed tomography angiography data
International Symposium on Biomedical Imaging, 2007Co-Authors: Coert Metz, Michiel Schaap, A G Van Der Giessen, T Van Walsum, Wiro J NiessenAbstract:This paper presents a semi-automatic coronary Centerline extraction algorithm for computed tomography angiography data. The method applies region growing to computed tomography angiography data and incorporates bifurcation and leak detection. The presented method is evaluated either on the original data and on data in which vessel-like structures have been enhanced. Semi-automatically extracted Centerlines of the three main coronary arteries are compared with Centerlines manually annotated by three observers, using an overlap and distance measure. The method successfully extracted vessel Centerlines in up to 15 out of 18 evaluated cases, with a localization accuracy which was in the range of the interobserver variability. Vessel enhancement prior to Centerline extraction did not improve the results
Adrien Kaladji - One of the best experts on this subject based on the ideXlab platform.
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Centerline is not as accurate as outer curvature length to estimate thoracic endograft length
European Journal of Vascular and Endovascular Surgery, 2013Co-Authors: Adrien Kaladji, R Spear, Adrien Hertault, Jonathan Sobocinski, Blandine Maurel, Stephan HaulonAbstract:Background To assess the accuracy of the aortic outer curvature length for thoracic endograft planning. Methods Seventy-four patients (58 men, 66.4 ± 14 years) who underwent thoracic endovascular aortic repair between 2009 and 2011 treated with a Cook Medical endograft were enrolled in this retrospective study. Immediate postoperative CT scans were analysed using EndoSize software. Three vessel lengths were computed between two fixed landmarks placed at each end of the endograft: the straightline (axial) length, the Centerline length and the outer curvature length. A tortuosity index was defined as the ratio of the Centerline length/straightline length. A Student t test and a Pearson correlation coefficient were used to examine the results. Results We found a significant difference between the Centerline length (135.4 ± 24 mm) and that of the endograft (160 ± 29 mm) ( p r = .818, p r = .587, p r = .53, p p = .792). Conclusion The outer curvature length more accurately reflects that of the deployed endograft and may prove more accurate than Centerlines in planning thoracic endografts.
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Centerline is not as accurate as outer curvature length to estimate thoracic endograft length.
European Journal of Vascular and Endovascular Surgery, 2013Co-Authors: Adrien Kaladji, R Spear, Adrien Hertault, Jonathan Sobocinski, Blandine Maurel, Stephan HaulonAbstract:BACKGROUND: To assess the accuracy of the aortic outer curvature length for thoracic endograft planning. METHODS: Seventy-four patients (58 men, 66.4 ± 14 years) who underwent thoracic endovascular aortic repair between 2009 and 2011 treated with a Cook Medical endograft were enrolled in this retrospective study. Immediate postoperative CT scans were analysed using EndoSize software. Three vessel lengths were computed between two fixed landmarks placed at each end of the endograft: the straightline (axial) length, the Centerline length and the outer curvature length. A tortuosity index was defined as the ratio of the Centerline length/straightline length. A Student t test and a Pearson correlation coefficient were used to examine the results. RESULTS: We found a significant difference between the Centerline length (135.4 ± 24 mm) and that of the endograft (160 ± 29 mm) (p < .0001). This difference correlates with the tortuosity index (r = .818, p < .0001), the endograft length (r = .587, p < .0001), and the diameter of the endograft (r = .53, p < .0001). However, the outer curvature length (161.3 ± 29 mm) and the endograft length (160 ± 29 mm) were similar (p = .792). CONCLUSION: The outer curvature length more accurately reflects that of the deployed endograft and may prove more accurate than Centerlines in planning thoracic endografts.
Zelang Miao - One of the best experts on this subject based on the ideXlab platform.
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An integrated method for urban main-road Centerline extraction from optical remotely sensed imagery
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Wenzhong Shi, Zelang Miao, Johan DebayleAbstract:Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road Centerlines from satellite optical images. The proposed method has four main steps. First, general adaptive neighborhood is introduced to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups. Second, road groups and homogeneous property, measured by local Geary's C, are fused to improve road-group accuracy. Third, road shape features are used to extract reliable road segments. Finally, local linear kernel smoothing regression is performed to extract smooth road Centerlines. Road networks are then generated using tensor voting. The proposed method is tested and subsequently validated using a large set of multispectral high-resolution images. A comparison with several existing methods shows that the proposed method is more suitable for urban main-road Centerline extraction.
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road Centerline extraction from high resolution imagery based on shape features and multivariate adaptive regression splines
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Zelang Miao, Wenzhong Shi, Hua Zhang, Xinxin WangAbstract:Road Centerline extraction from remotely sensed imagery can be used to update a Geographic Information System (GIS) database. The common road extraction from high-resolution imagery is based on spectral information only; it is difficult to separate road features from background completely, and a thinning algorithm always results in short spurs which reduce the smoothness of the road Centerline. To overcome the aforementioned shortcomings of the common existing road Centerline algorithms, this letter presents a new method to extract the road Centerline from high-resolution imagery based on shape features and multivariate adaptive regression splines (MARS), in which potential road segments were obtained based on shape features and spectral feature, followed by MARS to extract road Centerlines. Two experiments are performed to evaluate the accuracy of the proposed method.
Wenzhong Shi - One of the best experts on this subject based on the ideXlab platform.
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An integrated method for urban main-road Centerline extraction from optical remotely sensed imagery
IEEE Transactions on Geoscience and Remote Sensing, 2014Co-Authors: Wenzhong Shi, Zelang Miao, Johan DebayleAbstract:Road information has a fundamental role in modern society. Road extraction from optical satellite images is an economic and efficient way to obtain and update a transportation database. This paper presents an integrated method to extract urban main-road Centerlines from satellite optical images. The proposed method has four main steps. First, general adaptive neighborhood is introduced to implement spectral-spatial classification to segment the images into two categories: road and nonroad groups. Second, road groups and homogeneous property, measured by local Geary's C, are fused to improve road-group accuracy. Third, road shape features are used to extract reliable road segments. Finally, local linear kernel smoothing regression is performed to extract smooth road Centerlines. Road networks are then generated using tensor voting. The proposed method is tested and subsequently validated using a large set of multispectral high-resolution images. A comparison with several existing methods shows that the proposed method is more suitable for urban main-road Centerline extraction.
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road Centerline extraction from high resolution imagery based on shape features and multivariate adaptive regression splines
IEEE Geoscience and Remote Sensing Letters, 2013Co-Authors: Zelang Miao, Wenzhong Shi, Hua Zhang, Xinxin WangAbstract:Road Centerline extraction from remotely sensed imagery can be used to update a Geographic Information System (GIS) database. The common road extraction from high-resolution imagery is based on spectral information only; it is difficult to separate road features from background completely, and a thinning algorithm always results in short spurs which reduce the smoothness of the road Centerline. To overcome the aforementioned shortcomings of the common existing road Centerline algorithms, this letter presents a new method to extract the road Centerline from high-resolution imagery based on shape features and multivariate adaptive regression splines (MARS), in which potential road segments were obtained based on shape features and spectral feature, followed by MARS to extract road Centerlines. Two experiments are performed to evaluate the accuracy of the proposed method.