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classifyber-project.html

@@ -45,7 +45,7 @@ <h2>Menu</h2>
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 							<h1>White matter fiber classification</h1>
-							<p>The aim of this project was to build a classifier able to classify 
+							<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
@@ -56,9 +56,9 @@ <h1>White matter fiber classification</h1>
 								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 supervised method that performs automatic tract segmentation by learning
-								from example tracts segmented by experts. In particular, Classifyber provides a linear 
-								classifier that accurately predicts whether or not a given streamline (i.e. fiber) belongs 
+							<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
@@ -69,7 +69,7 @@ <h1>White matter fiber classification</h1>
 								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 open source web app through the platorm 
+								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>