A client-server architecture for semi-automatic segmentation of peripheral vessels in CTA data

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1 A client-server architecture for semi-automatic segmentation of peripheral vessels in CTA data Poster No.: C-2174 Congress: ECR 2013 Type: Authors: Keywords: DOI: Scientific Exhibit A. Grünauer, E. Vuçini, M. Trapp, K. Bühler; Vienna/AT Computer Applications-Detection, diagnosis, CAD, CT- Angiography, Extremities, Computer applications, Arteries / Aorta, Arteriosclerosis /ecr2013/C-2174 Any information contained in this pdf file is automatically generated from digital material submitted to EPOS by third parties in the form of scientific presentations. References to any names, marks, products, or services of third parties or hypertext links to thirdparty sites or information are provided solely as a convenience to you and do not in any way constitute or imply ECR's endorsement, sponsorship or recommendation of the third party, information, product or service. ECR is not responsible for the content of these pages and does not make any representations regarding the content or accuracy of material in this file. As per copyright regulations, any unauthorised use of the material or parts thereof as well as commercial reproduction or multiple distribution by any traditional or electronically based reproduction/publication method ist strictly prohibited. You agree to defend, indemnify, and hold ECR harmless from and against any and all claims, damages, costs, and expenses, including attorneys' fees, arising from or related to your use of these pages. Please note: Links to movies, ppt slideshows and any other multimedia files are not available in the pdf version of presentations. Page 1 of 9

2 Purpose Computed tomography angiography (CTA) allows the detailed assessment of the peripheral arteries and diagnosis of stenosis. For the support of the radiologist with computer-assisted diagnosis, robust and time-efficient methods for the segmentation of vessel are required. We propose a robust segmentation framework for the peripheral arteries in CTA data by combining the highly accurate tracking method of Zambal et al. [1] with a segment linking similar to the method of Bauer et al. [2]. We present a client-server application that allows minimal waiting time for the user. Methods and Materials Our method: The approach consists of a three-step procedure. 1. Seed calculation: We estimate the axis of vessel cross sections at each voxel with a significant gradient magnitude by a first-hit ray casting approach. If the obtained vessel diameter is within the specified vessel scale, we calculate the perpendicular axis at the estimated centre point by using the same ray-casting method (see Fig. 1 on page 3). This leads to a set of vessel seeds (see Fig. 2 on page 3), defined by centre point, radius and orientation. 2. Segment tracking: For each obtained vessel seed, a vessel segment is tracked (see Fig. 3 on page 4). A cylindrical shape model is initially fitted at the vessel seed location using a histogrambased evaluation function. We iteratively apply the model at both ends of the segment until a termination criterion is reached. 3. Tree growing: The algorithm connects the pre-computed vessel segments at user-defined start points to obtain the desired artery trees of interest (see Fig. 4 on page 5). At each given start point, the closest vessel segment represents the initial vessel tree. We stepwise extend the vessel tree by a neighbouring vessel segment once the requirements of any Page 2 of 9

3 connection rule are fulfilled. We defined three types of connection rules: end-to-end points, end-to-inner points and inner-to-end points (see Fig. 5 on page 6 ). Client-server architecture: One of our goals is a system that can be used within an interactive radiology application, where computational results need to be done within a few seconds. Therefore the first two steps are pre-computed on the server-side of our system, as no user-interaction is required. On the client-application the interactive tree growing step is invoked after each position click of the user within the desired response time. Images for this section: Fig. 1: First-hit ray casting for estimating first axis (left) and a perpendicular second axis (right) of vessel profiles Page 3 of 9

4 Fig. 2: Result of seed calculation: Visualized as circles in a schematic drawing (left) and as yellow points, calculated on CTA data (right). Page 4 of 9

5 Fig. 3: Result of vessel tracking: Set of unconnected vessel segments in schematic drawing (left) and calculated on CTA data (right). Page 5 of 9

6 Fig. 4: Resulting vessel tree: Visualized in schematic drawing (left) and as tree of vessel segments calculated on CTA data (right). Page 6 of 9

7 Fig. 5: The three types of connection rules: end-to-end points (left), end-to-inner points (center) and inner-to-end points (right). Page 7 of 9

8 Results We tested our algorithm in 10 peripheral CTA datasets. The centre lines of important vessels were manually annotated. We achieved a centre line accuracy of 0.51mm, and a precision and recall of 75.96% and 81.48%, respectively. One click was performed for 6 datasets and two clicks were used for the other 4 datasets. Recall rates of up to 93% were achieved with a higher number of clicks. The average pre-processing time required for an average dataset with 1127 slices of resolution 512x512 was 9 minutes and 30 seconds. The total average time needed for the execution on the client-side was 4.3s. Conclusion Our vessel tracking method achieves high accuracy and is robust to different CTA protocols. The client-server architecture allows minimal waiting time for the radiologist. In most cases, one click is sufficient to segment the peripheral artery tree References [1] Zambal S, Hladuvka H, Kanitsar A, Bühler K. Shape and appearance models for automatic coronary artery tracking. Proc MICCAI Workshop Grand Challenge Coronary Artery Tracking. 2008; p. Online Journal. [2] Bauer C, Bischof H. Edge based tube detection for coronary artery centerline extraction. Proc MICCAI Workshop Grand Challenge Coronary Artery Tracking. 2008;p. Online Journal. Personal Information Andreas Grünauer mailto: gruenauer (at) vrvis.at Page 8 of 9

9 Martin Trapp mailto: trapp (at) vrvis.at Katja Bühler mailto: buehler (at) vrvis.at VrVis Center for Virtual Reality and Visualization, Vienna, Austria Website: Page 9 of 9