Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas

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

doi:10.1007/s12021-016-9316-7

Cited by 39 publications

(42 citation statements)

References 35 publications

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“…The segmentation method calculates the distance d ME (equation ( 1 )) between each atlas bundle centroid and each fiber in the subject from the segmentation data set, normalized to the difference between the atlas centroid and the subject fiber lengths (Labra et al, 2017 ):

where dnf is a normalization factor that penalizes the length difference between the atlas centroids and the subject fibers. A restrictive threshold was used to label the short association bundles, thus selecting only the fibers that are very similar to the atlas bundles.…”

Section: Methodsmentioning

confidence: 99%

Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data

Román

Guevara

Valenzuela

et al. 2017

Front. Neuroinform.

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Human brain connectivity is extremely complex and variable across subjects. While long association and projection bundles are stable and have been deeply studied, short association bundles present higher intersubject variability, and few studies have been carried out to adequately describe the structure, shape, and reproducibility of these bundles. However, their analysis is crucial to understand brain function and better characterize the human connectome. In this study, we propose an automatic method to identify reproducible short association bundles of the superficial white matter, based on intersubject hierarchical clustering. The method is applied to the whole brain and finds representative clusters of similar fibers belonging to a group of subjects, according to a distance metric between fibers. We experimented with both affine and non-linear registrations and, due to better reproducibility, chose the results obtained from non-linear registration. Once the clusters are calculated, our method performs automatic labeling of the most stable connections based on individual cortical parcellations. We compare results between two independent groups of subjects from a HARDI database to generate reproducible connections for the creation of an atlas. To perform a better validation of the results, we used a bagging strategy that uses pairs of groups of 27 subjects from a database of 74 subjects. The result is an atlas with 44 bundles in the left hemisphere and 49 in the right hemisphere, of which 33 bundles are found in both hemispheres. Finally, we use the atlas to automatically segment 78 new subjects from a different HARDI database and to analyze stability and lateralization results.

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Section: Methodsmentioning

confidence: 99%

Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data

Román

Guevara

Valenzuela

et al. 2017

Front. Neuroinform.

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“…Garyfallidis et al proposed an alternative approach wherein the streamline atlas can be directly incorporated into the clustering process . Clayden et al and Labra et al also used streamline atlases in combination with a tract similarity measure to segment tracts (Clayden et al, 2007, Clayden et al, 2009, Labra et al, 2017.ROI-and clustering-based methods involve whole series of processing steps for atlas registration, tractography, parcellation or clustering. The resulting pipelines are rather complex, computationally expensive and tedious to fine-tune.…”

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confidence: 99%

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confidence: 99%

TractSeg - Fast and accurate white matter tract segmentation

Wasserthal¹,

Neher²,

2018

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The individual course of white matter fiber tracts is an important factor for analysis of white matter characteristics in healthy and diseased brains. Diffusion-weighted MRI tractography in combination with region-based or clustering-based selection of streamlines is a unique combination of tools which enables the in-vivo delineation and analysis of anatomically well-known tracts. This, however, currently requires complex, computationally intensive processing pipelines which take a lot of time to set up. TractSeg is a novel convolutional neural network-based approach that directly segments tracts in the field of fiber orientation distribution function (fODF) peaks without using tractography, image registration or parcellation. We demonstrate that the proposed approach is much faster than existing methods while providing unprecedented accuracy, using a population of 105 subjects from the Human Connectome Project. We also show initial evidence that TractSeg is able to generalize to differently acquired data sets for most of the bundles. The code and data are openly available at https://github.com/MIC-DKFZ/TractSeg/ and https://doi.org/10.5281/zenodo.1088277, respectively.

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“…In our case, the ideal distance measure therefore must always rank the pair of best‐matching fibers first, while for the use cases cited above, the absolute order is not that crucial. Analogously to Labra et al [LGD*17], we utilize multiple measures with differing accuracy and performance in order to speed up our computations. While in dMRI (for an overview see e.g., Schultz and Vilanova [SV18]) the input data are tensor fields or more general orientation fields from which the fibers have to be reconstructed with numerical methods, in our case the individual fibers are explicitly given.…”

Section: Background and Related Workmentioning

confidence: 99%

A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science

Fröhler

Elberfeld

Möller

et al. 2019

Computer Graphics Forum

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We present visual analysis methods for the evaluation of tomographic fiber reconstruction algorithms by means of analysis, visual debugging and comparison of reconstructed fibers in materials science. The methods are integrated in a tool (FIAKER) that supports the entire workflow. It enables the analysis of various fiber reconstruction algorithms, of differently parameterized fiber reconstruction algorithms and of individual steps in iterative fiber reconstruction algorithms. Insight into the performance of fiber reconstruction algorithms is obtained by a list‐based ranking interface. A 3D view offers interactive visualization techniques to gain deeper insight, e.g., into the aggregated quality of the examined fiber reconstruction algorithms and parameterizations. The tool was designed in close collaboration with researchers who work with fiber‐reinforced polymers on a daily basis and develop algorithms for tomographic reconstruction and characterization of such materials. We evaluate the tool using synthetic datasets as well as tomograms of real materials. Five case studies certify the usefulness of the tool, showing that it significantly accelerates the analysis and provides valuable insights that make it possible to improve the fiber reconstruction algorithms. The main contribution of the paper is the well‐considered combination of methods and their seamless integration into a visual tool that supports the entire workflow. Further findings result from the analysis of (dis‐)similarity measures for fibers as well as from the discussion of design decisions. It is also shown that the generality of the analytical methods allows a wider range of applications, such as the application in pore space analysis.

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Fast Automatic Segmentation of White Matter Streamlines Based on a Multi-Subject Bundle Atlas

Cited by 39 publications

References 35 publications

Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data

Clustering of Whole-Brain White Matter Short Association Bundles Using HARDI Data

TractSeg - Fast and accurate white matter tract segmentation

A Visual Tool for the Analysis of Algorithms for Tomographic Fiber Reconstruction in Materials Science

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