172 research outputs found
Joint Reconstruction and Parcellation of Cortical Surfaces
The reconstruction of cerebral cortex surfaces from brain MRI scans is
instrumental for the analysis of brain morphology and the detection of cortical
thinning in neurodegenerative diseases like Alzheimer's disease (AD). Moreover,
for a fine-grained analysis of atrophy patterns, the parcellation of the
cortical surfaces into individual brain regions is required. For the former
task, powerful deep learning approaches, which provide highly accurate brain
surfaces of tissue boundaries from input MRI scans in seconds, have recently
been proposed. However, these methods do not come with the ability to provide a
parcellation of the reconstructed surfaces. Instead, separate
brain-parcellation methods have been developed, which typically consider the
cortical surfaces as given, often computed beforehand with FreeSurfer. In this
work, we propose two options, one based on a graph classification branch and
another based on a novel generic 3D reconstruction loss, to augment
template-deformation algorithms such that the surface meshes directly come with
an atlas-based brain parcellation. By combining both options with two of the
latest cortical surface reconstruction algorithms, we attain highly accurate
parcellations with a Dice score of 90.2 (graph classification branch) and 90.4
(novel reconstruction loss) together with state-of-the-art surfaces.Comment: accepted at MLCN workshop 202
Non-Euclidean, convolutional learning on cortical brain surfaces
In recent years there have been many studies indicating that multiple cortical features, extracted at each surface vertex, are promising in the detection of various neurodevelopmental and neurodegenerative diseases. However, with limited datasets, it is challenging to train stable classifiers with such high-dimensional surface data. This necessitates a feature reduction that is commonly accomplished via regional volumetric morphometry from standard brain atlases. However, current regional summaries are not specific to the given age or pathology that is studied, which runs the risk of losing relevant information that can be critical in the classification process. To solve this issue, this paper proposes a novel data-driven approach by extending convolutional neural networks (CNN) for use on non-Euclidean manifolds such as cortical surfaces. The proposed network learns the most powerful features and brain regions from the extracted large dimensional feature space; thus creating a new feature space in which the dimensionality is reduced and feature distributions are better separated. We demonstrate the usability of the proposed surface-CNN framework in an example study classifying Alzheimers disease patients versus normal controls. The high performance in the cross-validation diagnostic results shows the potential of our proposed prediction system
SPHARM-Net: Spherical Harmonics-based Convolution for Cortical Parcellation
Department of Computer Science and EngineeringWe present a spherical harmonics-based convolutional neural network (CNN) for cortical parcellation, which we call SPHARM-Net. Recent advances in CNNs offer cortical parcellation on a fine-grained triangle mesh of the cortex. Yet, most CNNs designed for cortical parcellation employ spatial convolution that depends on extensive data augmentation and allows only predefined neighborhoods of specific spherical tessellation. On the other hand, a rotation-equivariant convolutional filter avoids data augmentation, and rotational equivariance can be achieved in spectral convolution independent of a neighborhood definition. Nevertheless, the limited resources of a modern machine enable only a finite set of spectral components that might lose geometric details. In this work, we propose (1) a constrained spherical convolutional filter that supports an infinite set of spectral components and (2) an end-to-end framework without data augmentation. The proposed filter encodes all the spectral components without the full expansion of spherical harmonics. We show that rotational equivariance drastically reduces the training time while achieving accurate cortical parcellation. Furthermore, the proposed convolution is fully composed of matrix transformations, which offers efficient and fast spectral processing. In the experiments, we validate SPHARM-Net on two public datasets with manual labels: Mindboggle-101 (N=101) and NAMIC (N=39). The experimental results show that the proposed method outperforms the state-of-the-art methods on both datasets even with fewer learnable parameters without rigid alignment and data augmentation.
Our code is publicly available at https://github.com/Shape-Lab/SPHARM-Net.ope
JOSA: Joint surface-based registration and atlas construction of brain geometry and function
Surface-based cortical registration is an important topic in medical image
analysis and facilitates many downstream applications. Current approaches for
cortical registration are mainly driven by geometric features, such as sulcal
depth and curvature, and often assume that registration of folding patterns
leads to alignment of brain function. However, functional variability of
anatomically corresponding areas across subjects has been widely reported,
particularly in higher-order cognitive areas. In this work, we present JOSA, a
novel cortical registration framework that jointly models the mismatch between
geometry and function while simultaneously learning an unbiased
population-specific atlas. Using a semi-supervised training strategy, JOSA
achieves superior registration performance in both geometry and function to the
state-of-the-art methods but without requiring functional data at inference.
This learning framework can be extended to any auxiliary data to guide
spherical registration that is available during training but is difficult or
impossible to obtain during inference, such as parcellations, architectonic
identity, transcriptomic information, and molecular profiles. By recognizing
the mismatch between geometry and function, JOSA provides new insights into the
future development of registration methods using joint analysis of the brain
structure and function.Comment: A. V. Dalca and B. Fischl are co-senior authors with equal
contribution. arXiv admin note: text overlap with arXiv:2303.0159
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