GPU-Based acceleration of an automatic white matter segmentation algorithm using CUDA

Labra, Nicole; Figueroa, Miguel; Guevara, Pamela; Duclap, Delphine; Hoeunou, Josselin; Poupon, Cyril; Mangin, Jean‐François

doi:10.1109/embc.2013.6609444

Cited by 2 publications

(1 citation statement)

References 5 publications

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“…Despite the higher level of accuracy and valuable information that tractography can provide in medical diagnosis procedures, its high complexity and huge size remains a great challenge [46]. Run time analysis of network usually depends on the features in the network, the strength of the implemented algorithm, the number of nodes and the edges in the populated datasets, and also the processing capability of the hardware utilized.…”

Section: Hardware-accelerated Approach In Visualizationmentioning

confidence: 99%

ConnectViz: Accelerated Approach for Brain Structural Connectivity Using Delaunay Triangulation

Adeshina

Hashim

2015

Interdiscip Sci Comput Life Sci

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Stroke is a cardiovascular disease with high mortality and long-term disability in the world. Normal functioning of the brain is dependent on the adequate supply of oxygen and nutrients to the brain complex network through the blood vessels. Stroke, occasionally a hemorrhagic stroke, ischemia or other blood vessel dysfunctions can affect patients during a cerebrovascular incident. Structurally, the left and the right carotid arteries, and the right and the left vertebral arteries are responsible for supplying blood to the brain, scalp and the face. However, a number of impairment in the function of the frontal lobes may occur as a result of any decrease in the flow of the blood through one of the internal carotid arteries. Such impairment commonly results in numbness, weakness or paralysis. Recently, the concepts of brain's wiring representation, the connectome, was introduced. However, construction and visualization of such brain network requires tremendous computation. Consequently, previously proposed approaches have been identified with common problems of high memory consumption and slow execution. Furthermore, interactivity in the previously proposed frameworks for brain network is also an outstanding issue. This study proposes an accelerated approach for brain connectomic visualization based on graph theory paradigm using compute unified device architecture, extending the previously proposed SurLens Visualization and computer aided hepatocellular carcinoma frameworks. The accelerated brain structural connectivity framework was evaluated with stripped brain datasets from the Department of Surgery, University of North Carolina, Chapel Hill, USA. Significantly, our proposed framework is able to generate and extract points and edges of datasets, displays nodes and edges in the datasets in form of a network and clearly maps data volume to the corresponding brain surface. Moreover, with the framework, surfaces of the dataset were simultaneously displayed with the nodes and the edges. The framework is very efficient in providing greater interactivity as a way of representing the nodes and the edges intuitively, all achieved at a considerably interactive speed for instantaneous mapping of the datasets' features. Uniquely, the connectomic algorithm performed remarkably fast with normal hardware requirement specifications.

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Section: Hardware-accelerated Approach In Visualizationmentioning

confidence: 99%

ConnectViz: Accelerated Approach for Brain Structural Connectivity Using Delaunay Triangulation

Adeshina

Hashim

2015

Interdiscip Sci Comput Life Sci

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Interactive segmentation of white-matter fibers using a multi-subject atlas

Labra

Figueroa

Guevara

et al. 2014

2014 36th Annual International Conference of the IEEE Engineering in Medicine and Biology Society

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We present a fast algorithm for automatic segmentation of white matter fibers from tractography datasets based on a multi-subject bundle atlas. We describe a sequential version of the algorithm that runs on a desktop computer CPU, as well as a highly parallel version that uses a Graphics Processing Unit (GPU) as an accelerator. Our sequential implementation runs 270 times faster than a C++/Python implementation of a previous algorithm based on the same segmentation method, and 21 times faster than a highly optimized C version of the same previous algorithm. Our parallelized implementation exploits the multiple computation units and memory hierarchy of the GPU to further speed up the algorithm by a factor of 30 with respect to our sequential code. As a result, the time to segment a subject dataset of 800,000 fibers is reduced from more than 2.5 hours in the Python/C++ code, to less than one second in the GPU version.

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GPU-Based acceleration of an automatic white matter segmentation algorithm using CUDA

Cited by 2 publications

References 5 publications

ConnectViz: Accelerated Approach for Brain Structural Connectivity Using Delaunay Triangulation

ConnectViz: Accelerated Approach for Brain Structural Connectivity Using Delaunay Triangulation

Interactive segmentation of white-matter fibers using a multi-subject atlas

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