Spectral Forests: Learning of Surface Data, Application to Cortical Parcellation
Résumé
This paper presents a new method for classifying surface data
via spectral representations of shapes. Our approach benefits classification
problems that involve data living on surfaces, such as in cortical parcellation.
For instance, current methods for labeling cortical points into surface parcels
often involve a slow mesh deformation toward pre-labeled atlases, requiring
as much as 4 hours with the established FreeSurfer. This may burden neuroscience
studies involving region-specific measurements. Learning techniques
offer an attractive computational advantage, however, their representation of
spatial information, typically defined in a Euclidean domain, may be inadequate
for cortical parcellation. Indeed, cortical data resides on surfaces that
are highly variable in space and shape. Consequently, Euclidean representations
of surface data may be inconsistent across individuals. We propose
to fundamentally change the spatial representation of surface data, by exploiting
spectral coordinates derived from the Laplacian eigenfunctions of
shapes. They have the advantage over Euclidean coordinates, to be geometry
aware and to parameterize surfaces explicitly. This change of paradigm,
from Euclidean to spectral representations, enables a classifier to be applied
directly on surface data via spectral coordinates. In this paper, we decide to
build upon the successful Random Decision Forests algorithm and improve its
spatial representation with spectral features. Our method, Spectral Forests,
is shown to significantly improve the accuracy of cortical parcellations over
standard Random Decision Forests (74% versus 28% Dice overlaps), and produce
accuracy equivalent to FreeSurfer in a fraction of its time (23 seconds
versus 3 to 4 hours).
Domaines
Informatique [cs]
Origine : Fichiers produits par l'(les) auteur(s)
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