Data-Parallel MRI Brain Segmentation in Clinicial Use

  • Structural MRI brain analysis and segmentation is a crucial part in the daily routine in neurosurgery for intervention planning. Exemplarily, the free software FSL-FAST (FMRIB’s Segmentation Library – FMRIB’s Automated Segmentation Tool) in version 4 is used for segmentation of brain tissue types. To speed up the segmentation procedure by parallel execution, we transferred FSL-FAST to a General Purpose Graphics Processing Unit (GPGPU) using Open Computing Language (OpenCL) [1]. The necessary steps for parallelization resulted in substantially different and less useful results. Therefore, the underlying methods were revised and adapted yielding computational overhead. Nevertheless, we achieved a speed-up factor of 3.59 from CPU to GPGPU execution, as well providing similar useful or even better results.

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Metadaten
Author:Joachim Weber, Christian Doenitz, Alexander Brawanski, Christoph PalmORCiDGND
DOI:https://doi.org/10.1007/978-3-662-46224-9_67
Parent Title (German):Bildverarbeitung für die Medizin 2015; Algorithmen - Systeme - Anwendungen; Proceedings des Workshops vom 15. bis 17. März 2015 in Lübeck
Subtitle (German):Porting FSL-Fastv4 to GPGPUs
Publisher:Springer
Place of publication:Berlin
Document Type:conference proceeding (article)
Language:English
Year of first Publication:2015
Release Date:2019/12/20
Tag:Brain Segmentation; General Purpose Graphic Processing Unit; Magnetic Resonance Imaging; Parallel Execution; Voxel Spacing
GND Keyword:Kernspintomografie; Gehirn; Bildsegmentierung; Parallelverarbeitung
First Page:389
Last Page:394
Institutes:Fakultät Informatik und Mathematik
Regensburg Center of Biomedical Engineering - RCBE
Regensburg Medical Image Computing - ReMIC
Dewey Decimal Classification:0 Informatik, Informationswissenschaft, allgemeine Werke