Alignment of Tractograms As Graph Matching

Olivetti, Emanuele; Sharmin, Nusrat; Avesani, Paolo

doi:10.3389/fnins.2016.00554

Cited by 18 publications

(21 citation statements)

References 39 publications

(67 reference statements)

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“…A different approach based on graph matching, instead of the nearest neighbor, was proposed by us for tractogram alignment, see Olivetti et al ( 2016 ). Such idea could be extended to the tract segmentation problem.…”

Section: Related Workmentioning

confidence: 99%

White Matter Tract Segmentation as Multiple Linear Assignment Problems

2018

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Diffusion magnetic resonance imaging (dMRI) allows to reconstruct the main pathways of axons within the white matter of the brain as a set of polylines, called streamlines. The set of streamlines of the whole brain is called the tractogram. Organizing tractograms into anatomically meaningful structures, called tracts, is known as the tract segmentation problem, with important applications to neurosurgical planning and tractometry. Automatic tract segmentation techniques can be unsupervised or supervised. A common criticism of unsupervised methods, like clustering, is that there is no guarantee to obtain anatomically meaningful tracts. In this work, we focus on supervised tract segmentation, which is driven by prior knowledge from anatomical atlases or from examples, i.e., segmented tracts from different subjects. We present a supervised tract segmentation method that segments a given tract of interest in the tractogram of a new subject using multiple examples as prior information. Our proposed tract segmentation method is based on the idea of streamline correspondence i.e., on finding corresponding streamlines across different tractograms. In the literature, streamline correspondence has been addressed with the nearest neighbor (NN) strategy. Differently, here we formulate the problem of streamline correspondence as a linear assignment problem (LAP), which is a cornerstone of combinatorial optimization. With respect to the NN, the LAP introduces a constraint of one-to-one correspondence between streamlines, that forces the correspondences to follow the local anatomical differences between the example and the target tract, neglected by the NN. In the proposed solution, we combined the Jonker-Volgenant algorithm (LAPJV) for solving the LAP together with an efficient way of computing the nearest neighbors of a streamline, which massively reduces the total amount of computations needed to segment a tract. Moreover, we propose a ranking strategy to merge correspondences coming from different examples. We validate the proposed method on tractograms generated from the human connectome project (HCP) dataset and compare the segmentations with the NN method and the ROI-based method. The results show that LAP-based segmentation is vastly more accurate than ROI-based segmentation and substantially more accurate than the NN strategy. We provide a Free/OpenSource implementation of the proposed method.

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Section: Related Workmentioning

confidence: 99%

White Matter Tract Segmentation as Multiple Linear Assignment Problems

2018

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“…We adopted the white matter query language (WMQL) [14] to obtain 9 segmented bundles for 10 random subjects, which we considered as ground truth. We selected the bundles reproducing the selection in [12], where they aimed to avoid extreme variability of the same bundle across subjects, due to the limitations of WMQL. The selected bundles are reported in the first column of Table I.…”

Section: Methodsmentioning

confidence: 99%

“…In this work, we propose to address the gap in the literature by providing guidelines for the choice of the streamlinestreamline distance function for the specific task of supervised bundle segmentation. Following the ideas in [4], [5], [12], we adopt the supervised segmentation framework, where the desired bundle is automatically segmented from a tractogram starting from an example of that bundle segmented by an expert on a different subject.…”

Section: Introductionmentioning

confidence: 99%

Comparison of distances for supervised segmentation of white matter tractography

Olivetti

Bertó

Gori

et al. 2017

2017 International Workshop on Pattern Recognition in Neuroimaging (PRNI)

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Abstract-Tractograms are mathematical representations of the main paths of axons within the white matter of the brain, from diffusion MRI data. Such representations are in the form of polylines, called streamlines, and one streamline approximates the common path of tens of thousands of axons. The analysis of tractograms is a task of interest in multiple fields, like neurosurgery and neurology. A basic building block of many pipelines of analysis is the definition of a distance function between streamlines. Multiple distance functions have been proposed in the literature, and different authors use different distances, usually without a specific reason other than invoking the "common practice". To this end, in this work we want to test such common practices, in order to obtain factual reasons for choosing one distance over another. For these reason, in this work we compare many streamline distance functions available in the literature. We focus on the common task of automatic bundle segmentation and we adopt the recent approach of supervised segmentation from expert-based examples. Using the HCP dataset, we compare several distances obtaining guidelines on the choice of which distance function one should use for supervised bundle segmentation.

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“…However, it generally does not hold for different subjects in cross-sectional studies. More recently, the tractogram matching problem has been cast as a graph matching problem 59 .…”

Section: Whole-brain Streamline Matching By Topographic Vector Alignmmentioning

confidence: 99%

A Measure of Whole-Brain White Matter Topography

Wang¹,

Shi²

2019

Preprint

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The unprecedentedly high-quality large-scale brain imaging datasets, from such as the Human Connectome Project (HCP) and UK-Biobank, provide a unique opportunity for measuring the white matter topography of the human brain. By leveraging the multi-shell diffusion MRI data from the original young adult HCP, we systematically develop a reliable measure of the whole-brain white matter topography, and we coin it topographic vector. As the main result, we find that the three most dominant dimensions of the topographic vectors strongly and linearly correlate with the coordinates of the corresponding streamlines of the whole-brain tractograms. Our results support the earlier prescient hypothesis that brain development follows a "base-plan" established by three (main) chemotactic gradients of early embryogenesis, and they implicate that the whole brain white matter tracts can be represented by vectors of a natural coordinate system. The topographic organisation, or topography, of the brain white matter was first measured using Klingler's brain dissection technique in early post-mortem studies 30 years ago 1 . However, our understanding of it is still limited. We view the topography of brain white matter pathways at two scales: local and global. The local topographic organisation, which is also known as the topographic regularity, has been defined as the preservation of the spatial relationship between neurons in point-to-point or region-toregion axonal connections 2,3 and it has been supported by tracer injection results 4-7 and diffusion magnetic resonance imaging (dMRI) studies 8-11 for various white matter

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Alignment of Tractograms As Graph Matching

Cited by 18 publications

References 39 publications

White Matter Tract Segmentation as Multiple Linear Assignment Problems

White Matter Tract Segmentation as Multiple Linear Assignment Problems

Comparison of distances for supervised segmentation of white matter tractography

A Measure of Whole-Brain White Matter Topography

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