6 research outputs found
Unsupervised Multimodal Surface Registration with Geometric Deep Learning
This paper introduces GeoMorph, a novel geometric deep-learning framework
designed for image registration of cortical surfaces. The registration process
consists of two main steps. First, independent feature extraction is performed
on each input surface using graph convolutions, generating low-dimensional
feature representations that capture important cortical surface
characteristics. Subsequently, features are registered in a deep-discrete
manner to optimize the overlap of common structures across surfaces by learning
displacements of a set of control points. To ensure smooth and biologically
plausible deformations, we implement regularization through a deep conditional
random field implemented with a recurrent neural network. Experimental results
demonstrate that GeoMorph surpasses existing deep-learning methods by achieving
improved alignment with smoother deformations. Furthermore, GeoMorph exhibits
competitive performance compared to classical frameworks. Such versatility and
robustness suggest strong potential for various neuroscience applications
Robust and Generalisable Segmentation of Subtle Epilepsy-causing Lesions: a Graph Convolutional Approach
Focal cortical dysplasia (FCD) is a leading cause of drug-resistant focal
epilepsy, which can be cured by surgery. These lesions are extremely subtle and
often missed even by expert neuroradiologists. "Ground truth" manual lesion
masks are therefore expensive, limited and have large inter-rater variability.
Existing FCD detection methods are limited by high numbers of false positive
predictions, primarily due to vertex- or patch-based approaches that lack
whole-brain context. Here, we propose to approach the problem as semantic
segmentation using graph convolutional networks (GCN), which allows our model
to learn spatial relationships between brain regions. To address the specific
challenges of FCD identification, our proposed model includes an auxiliary loss
to predict distance from the lesion to reduce false positives and a weak
supervision classification loss to facilitate learning from uncertain lesion
masks. On a multi-centre dataset of 1015 participants with surface-based
features and manual lesion masks from structural MRI data, the proposed GCN
achieved an AUC of 0.74, a significant improvement against a previously used
vertex-wise multi-layer perceptron (MLP) classifier (AUC 0.64). With
sensitivity thresholded at 67%, the GCN had a specificity of 71% in comparison
to 49% when using the MLP. This improvement in specificity is vital for
clinical integration of lesion-detection tools into the radiological workflow,
through increasing clinical confidence in the use of AI radiological adjuncts
and reducing the number of areas requiring expert review.Comment: accepted at MICCAI 202
A Deep-Discrete Learning Framework for Spherical Surface Registration
Cortical surface registration is a fundamental tool for neuroimaging analysis
that has been shown to improve the alignment of functional regions relative to
volumetric approaches. Classically, image registration is performed by
optimizing a complex objective similarity function, leading to long run times.
This contributes to a convention for aligning all data to a global average
reference frame that poorly reflects the underlying cortical heterogeneity. In
this paper, we propose a novel unsupervised learning-based framework that
converts registration to a multi-label classification problem, where each point
in a low-resolution control grid deforms to one of fixed, finite number of
endpoints. This is learned using a spherical geometric deep learning
architecture, in an end-to-end unsupervised way, with regularization imposed
using a deep Conditional Random Field (CRF). Experiments show that our proposed
framework performs competitively, in terms of similarity and areal distortion,
relative to the most popular classical surface registration algorithms and
generates smoother deformations than other learning-based surface registration
methods, even in subjects with atypical cortical morphology.Comment: 13 page
Surface Analysis with Vision Transformers
The extension of convolutional neural networks (CNNs) to non-Euclidean
geometries has led to multiple frameworks for studying manifolds. Many of those
methods have shown design limitations resulting in poor modelling of long-range
associations, as the generalisation of convolutions to irregular surfaces is
non-trivial. Recent state-of-the-art performance of Vision Transformers (ViTs)
demonstrates that a general-purpose architecture, which implements
self-attention, could replace the local feature learning operations of CNNs.
Motivated by the success of attention-modelling in computer vision, we extend
ViTs to surfaces by reformulating the task of surface learning as a
sequence-to-sequence problem and propose a patching mechanism for surface
meshes. We validate the performance of the proposed Surface Vision Transformer
(SiT) on two brain age prediction tasks in the developing Human Connectome
Project (dHCP) dataset and investigate the impact of pre-training on model
performance. Experiments show that the SiT outperforms many surface CNNs, while
indicating some evidence of general transformation invariance. Code available
at https://github.com/metrics-lab/surface-vision-transformersComment: 7 pages, 1 figure, accepted to Transformers for Vision (T4V) workshop
at CVPR 2022. arXiv admin note: substantial text overlap with
arXiv:2204.03408, arXiv:2203.1641