21 March 2007 Distributed video generation on a GPU-cluster for the web-based analysis of medical image data
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Abstract
Modern 3D visualization environments for medical image data provide high interactivity and flexibility but depend on the expert knowledge and the experience of the user with respect to the software application. The definition of the visualization parameters is a manual time-consuming process and as a result, inter-patient or inter-study comparisons are extremely difficult. To overcome these drawbacks in case of the analysis and diagnosis of pathologies, standardization of 3D visualization is an important issue. For this purpose automatically generated digital video sequences can be used to convey the most important information contained in the data. In this paper, we present an improvement of our existing web-based service which is now able to calculate the video sequences in much shorter time exploiting the power of a GPU-cluster. The system requires to transfer a medical volume dataset from an arbitrary computer connected via Internet and sends back a number of video files automatically generated with direct volume rendering. To achieve an optimal load balancing of the available resources, the tasks of automatic adjustment of transfer functions, volume rendering, and video encoding are divided into small sub-requests, which are distributed to the different cluster nodes in order to be performed in parallel. An additional preview mode, which renders a number of dedicated frames, provides a direct feedback and quick overview. For the evaluation, we were focusing on the analysis of intracranial aneurysms and were able to show that the system can be successfully applied. Further on, the system was developed in a way that allows easy integration of other analysis tasks.
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Friedemann Rößler, Torsten Wolff, Sabine Iserhardt-Bauer, Bernd Tomandl, Peter Hastreiter, Thomas Ertl, "Distributed video generation on a GPU-cluster for the web-based analysis of medical image data", Proc. SPIE 6509, Medical Imaging 2007: Visualization and Image-Guided Procedures, 650903 (21 March 2007); doi: 10.1117/12.710058; https://doi.org/10.1117/12.710058
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