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classifyber-project.html
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<!DOCTYPE HTML>
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<h1>White matter fiber classification</h1>
<p>The aim of this project was to build a <b>classifier</b> able to classify
white matter fibers into anatomically meaningful white matter tracts
of the human brain. </p>
<p>Obtaining accurately segmented white matter tracts in the human brain is essential for
multiple applications, such as pre-surgical planning and tractometry. Although notable
improvements have occurred over the years, the segmentation quality is not yet satisfactory,
especially when dealing with datasets with diverse characteristics, such as different
tract properties, tracking methods, or data quality. To overcome these limitations,
we proposed a novel supervised segmentation method, called Classifyber, which combines
in a simple linear model information from the geometry of the fiber paths, from their
connectivity patterns, and from anatomy.</p>
<p>Classifyber is a <b>supervised method</b> that performs <b>automatic tract segmentation</b> by learning
from example tracts segmented by experts. In particular, Classifyber provides a <b>linear
classifier</b> that accurately predicts whether or not a given streamline (i.e. fiber) belongs
to the tract of interest. In order to create the linear classifier, first we
transform each streamline into a vector that contains both its geometrical and anatomical
information. Then, we train a linear classifier, specifically Logistic Regression, with such
vectors from multiple participants in which experts segmented the tracts of interest.</p>
<span class="image main"><img src="images/ohbm2020_poster_Giulia_Berto_img4.png" alt="" /></span>
<p>We ran multiple experiments over four different datasets, and compared the performances of
our method, Classifyber, with other three state-of-the-art- tract segmentation methods through
the Dice Similarity Coefficient (DSC) score (the higher the better). Classifyber outperformed
the other methods in all cases, and segmented the tracts very accurately. This occured across
different kinds of tracts, tractography techniques, expert-made segmentations, and data
quality. Classifyber is freely available as an <b>open source web app</b> through the platorm
brainlife.io.</p>
<span class="image main"><img src="images/bar_plot.png" alt="" /></span>
<p>Read the full paper here: <a href=https://pubmed.ncbi.nlm.nih.gov/32979520/>https://pubmed.ncbi.nlm.nih.gov/32979520/</a>
<br>Read the poster here: <a href=https://github.com/FBK-NILab/app-classifyber-segmentation/blob/master/ohbm2020_poster.pdf>classifyber-poster.pdf</a>
<br>Try the brainlife App here: <a href=https://doi.org/10.25663/brainlife.app.265>https://doi.org/10.25663/brainlife.app.265</a>
<br>Code: <a href=https://github.com/FBK-NILab/app-classifyber-segmentation/tree/master>app-classifyber-segmentation</a></p>
<p>Citation: <i>Bertò G, Bullock D, Astolfi P, Hayashi S, Zigiotto L, Annicchiarico L, Corsini F,
De Benedictis A, Sarubbo S, Pestilli F, Avesani P, Olivetti E. Classifyber, a robust
streamline-based linear classifier for white matter bundle segmentation.
Neuroimage. 2021 Jan 1;224:117402. doi: 10.1016/j.neuroimage.2020.117402. Epub 2020 Sep 23.
PMID: 32979520.</i></p>
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