Tractography filtering using autoencoders

by   Jon Haitz Legarreta, et al.

Current brain white matter fiber tracking techniques show a number of problems, including: generating large proportions of streamlines that do not accurately describe the underlying anatomy; extracting streamlines that are not supported by the underlying diffusion signal; and under-representing some fiber populations, among others. In this paper, we describe a novel unsupervised learning method to filter streamlines from diffusion MRI tractography, and hence, to obtain more reliable tractograms. We show that a convolutional neural network autoencoder provides a straightforward and elegant way to learn a robust representation of brain streamlines, which can be used to filter undesired samples with a nearest neighbor algorithm. Our method, dubbed FINTA (Filtering in Tractography using Autoencoders) comes with several key advantages: training does not need labeled data, as it uses raw tractograms, it is fast and easily reproducible, it does not rely on the input diffusion MRI data, and thus, does not suffer from domain adaptation issues. We demonstrate the ability of FINTA to discriminate between "plausible" and "implausible" streamlines as well as to recover individual streamline group instances from a raw tractogram, from both synthetic and real human brain diffusion MRI tractography data, including partial tractograms. Results reveal that FINTA has a superior filtering performance compared to state-of-the-art methods. Together, this work brings forward a new deep learning framework in tractography based on autoencoders, and shows how it can be applied for filtering purposes. It sets the foundations for opening up new prospects towards more accurate and robust tractometry and connectivity diffusion MRI analyses, which may ultimately lead to improve the imaging of the white matter anatomy.


page 7

page 10

page 12

page 14

page 16

page 24

page 25

page 27


A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling

While diffusion MRI has been extremely promising in the study of MTBI, i...

Direct segmentation of brain white matter tracts in diffusion MRI

The brain white matter consists of a set of tracts that connect distinct...

Supervised Tractogram Filtering using Geometric Deep Learning

A tractogram is a virtual representation of the brain white matter. It i...

Reconstructing the somatotopic organization of the corticospinal tract remains a challenge for modern tractography methods

The corticospinal tract (CST) is a critically important white matter fib...

Tractogram filtering of anatomically non-plausible fibers with geometric deep learning

Tractograms are virtual representations of the white matter fibers of th...

FIESTA: FIber gEneration and bundle Segmentation in Tractography using Autoencoders

White matter bundle segmentation is a cornerstone of modern tractography...

Generative sampling in tractography using autoencoders (GESTA)

Current tractography methods use the local orientation information to pr...

Please sign up or login with your details

Forgot password? Click here to reset