Supervised Segmentation of Fiber Tracts

author: Emanuele Olivetti, Bruno Kessler Foundation
published: Oct. 17, 2011,   recorded: September 2011,   views: 2775


Related Open Educational Resources

Related content

Report a problem or upload files

If you have found a problem with this lecture or would like to send us extra material, articles, exercises, etc., please use our ticket system to describe your request and upload the data.
Enter your e-mail into the 'Cc' field, and we will keep you updated with your request's status.
Lecture popularity: You need to login to cast your vote.


In this work we study the problem of supervised tract segmentation from tractography data, a vectorial representation of the brain connectivity extracted from diffusion magnetic resonance images. We report a case study based on a dataset where for each tractography of three subjects the segmentation of eight major anatomical tracts was manually operated by expert neuroanatomists. Domain specific distances that encodes the dissimilarity of tracts do not allow to define a positive semi-definite kernel function. We show that a dissimilarity representation based on such distances enables the successful design of a classifier. This approach provides a robust encoding which proves to be effective using a linear classifier. Our empirical analysis shows that we obtain better tract segmentation than previously proposed methods.

See Also:

Download slides icon Download slides: simbad2011_olivetti_segmentation_01.pdf (3.1┬áMB)

Help icon Streaming Video Help

Link this page

Would you like to put a link to this lecture on your homepage?
Go ahead! Copy the HTML snippet !

Write your own review or comment:

make sure you have javascript enabled or clear this field: