4 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
Anatomically-Informed Multiple Linear Assignment Problems for White Matter Bundle Segmentation
Segmenting white matter bundles from human tractograms is a task of interest
for several applications. Current methods for bundle segmentation consider
either only prior knowledge about the relative anatomical position of a bundle,
or only its geometrical properties. Our aim is to improve the results of
segmentation by proposing a method that takes into account information about
both the underlying anatomy and the geometry of bundles at the same time. To
achieve this goal, we extend a state-of-the-art example-based method based on
the Linear Assignment Problem (LAP) by including prior anatomical information
within the optimization process. The proposed method shows a significant
improvement with respect to the original method, in particular on small
bundles
Il modello lineare di Stark del sistema di controllo pupillare
Lo scopo del presente elaborato è quello di analizzare il servosistema pupillare umano secondo il modello lineare proposto da L. W. Stark focalizzando in particolar modo l'attenzione sullo studio della sua stabilità in diverse condizioni operative. Nel capitolo 1, per meglio comprendere i meccanismi che regolano il controllo pupillare, viene fornita una breve spiegazione riguardo all'anatomia dell'occhio umano e alla funzione del sistema nervoso autonomo, direttamente collegato ad esso. Nel capitolo 2 viene introdotto il modello linearizzato del sistema e viene descritto l'esperimento messo in pratica per ottenere i dati da analizzare. Nel capitolo 3 viene ricavata la funzione di trasferimento del sistema e viene studiata la sua stabilità tramite i criteri di Bode e di Nyquist. Nel capitolo 4 vengono presentati ulteriori modelli del servosistema pupillare che sono stati sviluppati successivamente al lavoro di L. W. Star
Classifyber, a robust streamline-based linear classifier for white matter bundle segmentation
Virtual delineation of white matter bundles in the human brain is of paramount importance for multiple applications, such as pre-surgical planning and connectomics. A substantial body of literature is related to methods that automatically segment bundles from diffusion Magnetic Resonance Imaging (dMRI) data indirectly, by exploiting either the idea of connectivity between regions or the geometry of fiber paths obtained with tractography techniques, or, directly, through the information in volumetric data. Despite the remarkable improvement in automatic segmentation methods over the years, their segmentation quality is not yet satisfactory, especially when dealing with datasets with very diverse characteristics, such as different tracking methods, bundle sizes or data quality. In this work, we propose a novel, supervised streamline-based segmentation method, called Classifyber, which combines information from atlases, connectivity patterns, and the geometry of fiber paths into a simple linear model. With a wide range of experiments on multiple datasets that span from research to clinical domains, we show that Classifyber substantially improves the quality of segmentation as compared to other state-of-the-art methods and, more importantly, that it is robust across very diverse settings. We provide an implementation of the proposed method as open source code, as well as web service