Parallel Optimization of Fiber Bundle Segmentation for Massive Tractography Datasets

Vázquez, Andrea; López-López, Narciso; Labra, Nicole; Figueroa, Miguel; Poupon, Cyril; Mangin, Jean‐François; Hernández, Cecilia; Guevara, Pamela

doi:10.1109/isbi.2019.8759208

Cited by 14 publications

(16 citation statements)

References 13 publications

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“…Segmenting the fibers provides direct correspondence of the bundles and the connected cortical regions across the subjects. The segmentation algorithm (Vázquez et al, 2019 ) is a parallel version of the algorithm proposed in Guevara et al ( 2012 ). It classifies the fibers of a subject's tractography based on a multi-subject WM bundle atlas.…”

Section: Methodsmentioning

confidence: 99%

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

López-López

Vázquez

Houenou

et al. 2020

Front. Neuroinform.

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In this article, we present a hybrid method to create fine-grained parcellations of the cortical surface, from a coarse-grained parcellation according to an anatomical atlas, based on cortico-cortical connectivity. The connectivity information is obtained from segmented superficial and deep white matter bundles, according to bundle atlases, instead of the whole tractography. Thus, a direct matching between the fiber bundles and the cortical regions is obtained, avoiding the problem of finding the correspondence of the cortical parcels among subjects. Generating parcels from segmented fiber bundles can provide a good representation of the human brain connectome since they are based on bundle atlases that contain the most reproducible short and long connections found on a population of subjects. The method first processes the tractography of each subject and extracts the bundles of the atlas, based on a segmentation algorithm. Next, the intersection between the fiber bundles and the cortical mesh is calculated, to define the initial and final intersection points of each fiber. A fiber filtering is then applied to eliminate misclassified fibers, based on the anatomical definition of each bundle and the labels of Desikan-Killiany anatomical parcellation. A parcellation algorithm is then performed to create a subdivision of the anatomical regions of the cortex, which is reproducible across subjects. This step resolves the overlapping of the fiber bundle extremities over the cortical mesh within each anatomical region. For the analysis, the density of the connections and the degree of overlapping, is considered and represented with a graph. One of our parcellations, an atlas composed of 160 parcels, achieves a reproducibility across subjects of ≈0.74, based on the average Dice's coefficient between subject's connectivity matrices, rather than ≈0.73 obtained for a macro anatomical parcellation of 150 parcels. Moreover, we compared two of our parcellations with state-of-the-art atlases, finding a degree of similarity with dMRI, functional, anatomical, and multi-modal atlases. The higher similarity was found for our parcellation composed of 185 sub-parcels with another parcellation based on dMRI data from the same database, but created with a different approach, leading to 130 parcels in common based on a Dice's coefficient ≥0.5.

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Section: Methodsmentioning

confidence: 99%

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

López-López

Vázquez

Houenou

et al. 2020

Front. Neuroinform.

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“…As observed, FFClust exploits CPU parallelism in all the Steps, where Step 1, 2 and 4 uses python Multiprocessing package for process-based parallelism. In Step 3, FFClust uses parallelism using C with OpenMP (Vázquez et al, 2019 ). The GPGPU implementation uses C++ and CUDA in Step 1, CUDA Thrust library in Step 2 and 3, and C++ and OpenMP in Step 4.…”

Section: Resultsmentioning

confidence: 99%

“…In Step 3, FFClust defines a C and OpenMP parallel implementation (Vázquez et al, 2019 ), which is called from the python implementation. Our GPGPU implementation uses the GPU to exploit more parallelism since more work is performed to assign small preliminary clusters to large ones.…”

Section: Resultsmentioning

confidence: 99%

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Fiber Clustering Acceleration With a Modified Kmeans++ Algorithm Using Data Parallelism

Goicovich

Olivares

Román

et al. 2021

Front. Neuroinform.

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Fiber clustering methods are typically used in brain research to study the organization of white matter bundles from large diffusion MRI tractography datasets. These methods enable exploratory bundle inspection using visualization and other methods that require identifying brain white matter structures in individuals or a population. Some applications, such as real-time visualization and inter-subject clustering, need fast and high-quality intra-subject clustering algorithms. This work proposes a parallel algorithm using a General Purpose Graphics Processing Unit (GPGPU) for fiber clustering based on the FFClust algorithm. The proposed GPGPU implementation exploits data parallelism using both multicore and GPU fine-grained parallelism present in commodity architectures, including current laptops and desktop computers. Our approach implements all FFClust steps in parallel, improving execution times in all of them. In addition, our parallel approach includes a parallel Kmeans++ algorithm implementation and defines a new variant of Kmeans++ to reduce the impact of choosing outliers as initial centroids. The results show that our approach provides clustering quality results very similar to FFClust, and it requires an execution time of 3.5 s for processing about a million fibers, achieving a speedup of 11.5 times compared to FFClust.

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“…Hence, we could obtain an atlas (or model) of cortical parcels with similar connectivity profiles across a population of healthy subjects. Also, other information could be integrated, like fibers segmented with a bundle atlas [27], or data from other modalities, like fMRI.…”

Section: Discussionmentioning

confidence: 99%

Cortical surface parcellation based on intra-subject white matter fiber clustering

López-López¹,

Vázquez²,

Poupon³

et al. 2019

2019 IEEE CHILEAN Conference on Electrical, Electronics Engineering, Information and Communication Technologies (CHILECON)

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We present a hybrid method that performs the complete parcellation of the cerebral cortex of an individual, based on the connectivity information of the white matter fibers from a whole-brain tractography dataset. The method consists of five steps, first intra-subject clustering is performed on the brain tractography. The fibers that make up each cluster are then intersected with the cortical mesh and then filtered to discard outliers. In addition, the method resolves the overlapping between the different intersection regions (sub-parcels) throughout the cortex efficiently. Finally, a post-processing is done to achieve more uniform sub-parcels. The output is the complete labeling of cortical mesh vertices, representing the different cortex sub-parcels, with strong connections to other sub-parcels. We evaluated our method with measures of brain connectivity such as functional segregation (clustering coefficient), functional integration (characteristic path length) and small-world. Results in five subjects from ARCHI database show a good individual cortical parcellation for each one, composed of about 200 sub-parcels per hemisphere and complying with these connectivity measures.

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Parallel Optimization of Fiber Bundle Segmentation for Massive Tractography Datasets

Cited by 14 publications

References 13 publications

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

From Coarse to Fine-Grained Parcellation of the Cortical Surface Using a Fiber-Bundle Atlas

Fiber Clustering Acceleration With a Modified Kmeans++ Algorithm Using Data Parallelism

Cortical surface parcellation based on intra-subject white matter fiber clustering

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