Surface agnostic metrics for cortical volume segmentation and regression
File(s)
Accepted version
OA Location
Author(s)
Type
Conference Paper
Abstract
The cerebral cortex performs higher-order brain functions and is thus implicated in a range of cognitive disorders. Current analysis of cortical variation is typically performed by fitting surface mesh models to inner and outer cortical boundaries and investigating metrics such as surface area and cortical curvature or thickness. These, however, take a long time to run, and are sensitive to motion and image and surface resolution, which can prohibit their use in clinical settings. In this paper, we instead propose a machine learning solution, training a novel architecture to predict cortical thickness and curvature metrics from T2 MRI images, while additionally returning metrics of prediction uncertainty. Our proposed model is tested on a clinical cohort (Down Syndrome) for which surface-based modelling often fails. Results suggest that deep convolutional neural networks are a viable option to predict cortical metrics across a range of brain development stages and pathologies.
Date Issued
2021-10-01
Date Acceptance
2020-08-04
Citation
Lecture Notes in Computer Science, 2021, pp.3-12
ISBN
9783030668426
ISSN
0302-9743
Publisher
Springer
Start Page
3
End Page
12
Journal / Book Title
Lecture Notes in Computer Science
Copyright Statement
© 2020 Springer Nature Switzerland AG. The final publication is available at Springer via https://link.springer.com/chapter/10.1007/978-3-030-66843-3_1
Source
The 3rd Workshop on Machine Learning in Clinical Neuroimaging
Subjects
Artificial Intelligence & Image Processing
Publication Status
Published
Start Date
2020-10-04
Finish Date
2020-10-05
Coverage Spatial
Lima, Peru (virtual)
Date Publish Online
2020-12-31