471 research outputs found
Comparison of Distances for Supervised Segmentation of White Matter Tractography
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 reasons, 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
Analyse et reconstruction de faisceaux de la matière blanche
L'imagerie par résonance magnétique de diffusion (IRMd) est une modalité d'acquisition permettant de sonder les tissus biologiques et d'en extraire une variété d'informations sur le mouvement microscopique des molécules d'eau. Plus spécifiquement à l'imagerie médicale, l'IRMd permet l'investigation des structures fibreuses de nombreux organes et facilite la compréhension des processus cognitifs ou au diagnostic. Dans le domaine des neurosciences, l'IRMd est cruciale à l'exploration de la connectivité structurelle de la matière blanche.
Cette thèse s'intéresse plus particulièrement à la reconstruction de faisceaux de la matière blanche ainsi qu'à leur analyse. Toute la complexité du traitement des données commençant au scanneur jusqu'à la création d'un tractogramme est extrêmement importante. Par contre, l'application spécifique de reconstruction des faisceaux anatomiques plausibles est ultimement le véritable défi de l'IRMd. L'optimisation des paramètres de la tractographie, le processus de segmentation manuelle ou automatique ainsi que l'interprétation des résultats liée à ces faisceaux sont toutes des étapes du processus avec leurs lots de difficultés
BundleSeg: A versatile, reliable and reproducible approach to white matter bundle segmentation
This work presents BundleSeg, a reliable, reproducible, and fast method for
extracting white matter pathways. The proposed method combines an iterative
registration procedure with a recently developed precise streamline search
algorithm that enables efficient segmentation of streamlines without the need
for tractogram clustering or simplifying assumptions. We show that BundleSeg
achieves improved repeatability and reproducibility than state-of-the-art
segmentation methods, with significant speed improvements. The enhanced
precision and reduced variability in extracting white matter connections offer
a valuable tool for neuroinformatic studies, increasing the sensitivity and
specificity of tractography-based studies of white matter pathways
Parallel optimization of fiber bundle segmentation for massive tractography datasets
We present an optimized algorithm that performs automatic classification of
white matter fibers based on a multi-subject bundle atlas. We implemented a
parallel algorithm that improves upon its previous version in both execution
time and memory usage. Our new version uses the local memory of each processor,
which leads to a reduction in execution time. Hence, it allows the analysis of
bigger subject and/or atlas datasets. As a result, the segmentation of a
subject of 4,145,000 fibers is reduced from about 14 minutes in the previous
version to about 6 minutes, yielding an acceleration of 2.34. In addition, the
new algorithm reduces the memory consumption of the previous version by a
factor of 0.79.Comment: This research has received funding from the European Union's Horizon
2020 research and innovation programme under the Marie Sk{\l}odowska-Curie
Actions H2020-MSCA-RISE-2015 BIRDS GA No. 690941, CONICYT PFCHA/ DOCTORADO
NACIONAL/2016-21160342, CONICYT FONDECYT 1161427, CONICYT PIA/Anillo de
Investigaci\'on en Ciencia y Tecnolog\'ia ACT172121, CONICYT BASAL FB0008 and
from CONICYT Basal FB000
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