704 research outputs found
Multi-Head Graph Convolutional Network for Structural Connectome Classification
We tackle classification based on brain connectivity derived from diffusion
magnetic resonance images. We propose a machine-learning model inspired by
graph convolutional networks (GCNs), which takes a brain connectivity input
graph and processes the data separately through a parallel GCN mechanism with
multiple heads. The proposed network is a simple design that employs different
heads involving graph convolutions focused on edges and nodes, capturing
representations from the input data thoroughly. To test the ability of our
model to extract complementary and representative features from brain
connectivity data, we chose the task of sex classification. This quantifies the
degree to which the connectome varies depending on the sex, which is important
for improving our understanding of health and disease in both sexes. We show
experiments on two publicly available datasets: PREVENT-AD (347 subjects) and
OASIS3 (771 subjects). The proposed model demonstrates the highest performance
compared to the existing machine-learning algorithms we tested, including
classical methods and (graph and non-graph) deep learning. We provide a
detailed analysis of each component of our model
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
Learning the Effect of Registration Hyperparameters with HyperMorph
We introduce HyperMorph, a framework that facilitates efficient
hyperparameter tuning in learning-based deformable image registration.
Classical registration algorithms perform an iterative pair-wise optimization
to compute a deformation field that aligns two images. Recent learning-based
approaches leverage large image datasets to learn a function that rapidly
estimates a deformation for a given image pair. In both strategies, the
accuracy of the resulting spatial correspondences is strongly influenced by the
choice of certain hyperparameter values. However, an effective hyperparameter
search consumes substantial time and human effort as it often involves training
multiple models for different fixed hyperparameter values and may lead to
suboptimal registration. We propose an amortized hyperparameter learning
strategy to alleviate this burden by learning the impact of hyperparameters on
deformation fields. We design a meta network, or hypernetwork, that predicts
the parameters of a registration network for input hyperparameters, thereby
comprising a single model that generates the optimal deformation field
corresponding to given hyperparameter values. This strategy enables fast,
high-resolution hyperparameter search at test-time, reducing the inefficiency
of traditional approaches while increasing flexibility. We also demonstrate
additional benefits of HyperMorph, including enhanced robustness to model
initialization and the ability to rapidly identify optimal hyperparameter
values specific to a dataset, image contrast, task, or even anatomical region,
all without the need to retrain models. We make our code publicly available at
http://hypermorph.voxelmorph.net.Comment: Accepted for publication at the Journal of Machine Learning for
Biomedical Imaging (MELBA) at https://www.melba-journal.or
Quantification of volumetric morphometry and optical property in the cortex of human cerebellum at micrometer resolution
The surface of the human cerebellar cortex is much more tightly folded than the cerebral cortex. Volumetric analysis of cerebellar morphometry in magnetic resonance imaging studies suffers from insufficient resolution, and therefore has had limited impact on disease assessment. Automatic serial polarization-sensitive optical coherence tomography (as-PSOCT) is an emerging technique that offers the advantages of microscopic resolution and volumetric reconstruction of large-scale samples. In this study, we reconstructed multiple cubic centimeters of ex vivo human cerebellum tissue using as-PSOCT. The morphometric and optical properties of the cerebellar cortex across five subjects were quantified. While the molecular and granular layers exhibited similar mean thickness in the five subjects, the thickness varied greatly in the granular layer within subjects. Layer-specific optical property remained homogenous within individual subjects but showed higher cross-subject variability than layer thickness. High-resolution volumetric morphometry and optical property maps of human cerebellar cortex revealed by as-PSOCT have great potential to advance our understanding of cerebellar function and diseases
Task-Optimal Registration Cost Functions
In this paper, we propose a framework for learning the parameters of registration cost functions – such as the tradeoff between the regularization and image similiarity term – with respect to a specific task. Assuming the existence of labeled training data, we specialize the framework for the task of localizing hidden labels via image registration. We learn the parameters of the weighted sum of squared differences (wSSD) image similarity term that are optimal for the localization of Brodmann areas (BAs) in a new subject based on cortical geometry. We demonstrate state-of-the-art localization of V1, V2, BA44 and BA45
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