90 research outputs found
SC-VAE: Sparse Coding-based Variational Autoencoder
Learning rich data representations from unlabeled data is a key challenge
towards applying deep learning algorithms in downstream supervised tasks.
Several variants of variational autoencoders have been proposed to learn
compact data representaitons by encoding high-dimensional data in a lower
dimensional space. Two main classes of VAEs methods may be distinguished
depending on the characteristics of the meta-priors that are enforced in the
representation learning step. The first class of methods derives a continuous
encoding by assuming a static prior distribution in the latent space. The
second class of methods learns instead a discrete latent representation using
vector quantization (VQ) along with a codebook. However, both classes of
methods suffer from certain challenges, which may lead to suboptimal image
reconstruction results. The first class of methods suffers from posterior
collapse, whereas the second class of methods suffers from codebook collapse.
To address these challenges, we introduce a new VAE variant, termed SC-VAE
(sparse coding-based VAE), which integrates sparse coding within variational
autoencoder framework. Instead of learning a continuous or discrete latent
representation, the proposed method learns a sparse data representation that
consists of a linear combination of a small number of learned atoms. The sparse
coding problem is solved using a learnable version of the iterative shrinkage
thresholding algorithm (ISTA). Experiments on two image datasets demonstrate
that our model can achieve improved image reconstruction results compared to
state-of-the-art methods. Moreover, the use of learned sparse code vectors
allows us to perform downstream task like coarse image segmentation through
clustering image patches.Comment: 15 pages, 11 figures, and 3 table
Normative Modeling using Multimodal Variational Autoencoders to Identify Abnormal Brain Structural Patterns in Alzheimer Disease
Normative modelling is an emerging method for understanding the underlying
heterogeneity within brain disorders like Alzheimer Disease (AD) by quantifying
how each patient deviates from the expected normative pattern that has been
learned from a healthy control distribution. Since AD is a multifactorial
disease with more than one biological pathways, multimodal magnetic resonance
imaging (MRI) neuroimaging data can provide complementary information about the
disease heterogeneity. However, existing deep learning based normative models
on multimodal MRI data use unimodal autoencoders with a single encoder and
decoder that may fail to capture the relationship between brain measurements
extracted from different MRI modalities. In this work, we propose multi-modal
variational autoencoder (mmVAE) based normative modelling framework that can
capture the joint distribution between different modalities to identify
abnormal brain structural patterns in AD. Our multi-modal framework takes as
input Freesurfer processed brain region volumes from T1-weighted (cortical and
subcortical) and T2-weighed (hippocampal) scans of cognitively normal
participants to learn the morphological characteristics of the healthy brain.
The estimated normative model is then applied on Alzheimer Disease (AD)
patients to quantify the deviation in brain volumes and identify the abnormal
brain structural patterns due to the effect of the different AD stages. Our
experimental results show that modeling joint distribution between the multiple
MRI modalities generates deviation maps that are more sensitive to disease
staging within AD, have a better correlation with patient cognition and result
in higher number of brain regions with statistically significant deviations
compared to a unimodal baseline model with all modalities concatenated as a
single input.Comment: Medical Imaging Meets NeurIPS workshop in NeurIPS 202
Development of white matter fiber covariance networks supports executive function in youth
During adolescence, the brain undergoes extensive changes in white matter structure that support cognition. Data-driven approaches applied to cortical surface properties have led the field to understand brain development as a spatially and temporally coordinated mechanism that follows hierarchically organized gradients of change. Although white matter development also appears asynchronous, previous studies have relied largely on anatomical tract-based atlases, precluding a direct assessment of how white matter structure is spatially and temporally coordinated. Harnessing advances in diffusion modeling and machine learning, we identified 14 data-driven patterns of covarying white matter structure in a large sample of youth. Fiber covariance networks aligned with known major tracts, while also capturing distinct patterns of spatial covariance across distributed white matter locations. Most networks showed age-related increases in fiber network properties, which were also related to developmental changes in executive function. This study delineates data-driven patterns of white matter development that support cognition
The University of Pennsylvania Glioblastoma (UPenn-GBM) cohort: Advanced MRI, clinical, genomics, & radiomics
Glioblastoma is the most common aggressive adult brain tumor. Numerous studies have reported results from either private institutional data or publicly available datasets. However, current public datasets are limited in terms of: a) number of subjects, b) lack of consistent acquisition protocol, c) data quality, or d) accompanying clinical, demographic, and molecular information. Toward alleviating these limitations, we contribute the University of Pennsylvania Glioblastoma Imaging, Genomics, and Radiomics (UPenn-GBM) dataset, which describes the currently largest publicly available comprehensive collection of 630 patients diagnosed with de novo glioblastoma. The UPenn-GBM dataset includes (a) advanced multi-parametric magnetic resonance imaging scans acquired during routine clinical practice, at the University of Pennsylvania Health System, (b) accompanying clinical, demographic, and molecular information, (d) perfusion and diffusion derivative volumes, (e) computationally-derived and manually-revised expert annotations of tumor sub-regions, as well as (f) quantitative imaging (also known as radiomic) features corresponding to each of these regions. This collection describes our contribution towards repeatable, reproducible, and comparative quantitative studies leading to new predictive, prognostic, and diagnostic assessments
Deformable Medical Image Registration: A Survey
Deformable image registration is a fundamental task in medical image processing. Among its most important applications, one may cite: i) multi-modality fusion, where information acquired by different imaging devices or protocols is fused to facilitate diagnosis and treatment planning; ii) longitudinal studies, where temporal structural or anatomical changes are investigated; and iii) population modeling and statistical atlases used to study normal anatomical variability. In this technical report, we attempt to give an overview of deformable registration methods, putting emphasis on the most recent advances in the domain. Additional emphasis has been given to techniques applied to medical images. In order to study image registration methods in depth, their main components are identified and studied independently. The most recent techniques are presented in a systematic fashion. The contribution of this technical report is to provide an extensive account of registration techniques in a systematic manner.Le recalage déformable d'images est une des tâches les plus fondamentales dans l'imagerie médicale. Parmi ses applications les plus importantes, on compte: i) la fusion d' information provenant des différents types de modalités a n de faciliter le diagnostic et la planification du traitement; ii) les études longitudinales, oú des changements structurels ou anatomiques sont étudiées en fonction du temps; et iii) la modélisation de la variabilité anatomique normale d'une population et les atlas statistiques. Dans ce rapport de recherche, nous essayons de donner un aperçu des différentes méthodes du recalage déformables, en mettant l'accent sur les avancées les plus récentes du domaine. Nous avons particulièrement insisté sur les techniques appliquées aux images médicales. A n d'étudier les méthodes du recalage d'images, leurs composants principales sont d'abord identifiés puis étudiées de manière indépendante, les techniques les plus récentes étant classifiées en suivant un schéma logique déterminé. La contribution de ce rapport de recherche est de fournir un compte rendu détaillé des techniques de recalage d'une manière systématique
MRF-based Diffeomorphic Population Deformable Registration & Segmentation
In this report, we present a novel framework to deform mutually a population of n-examples based on an optimality criterion. The optimality criterion comprises three terms, one that aims to impose local smoothness, a second that aims to minimize the individual distances between all possible pairs of images, while the last one is a global statistical measurement based on "compactness" criteria. The problem is reformulated using a discrete MRF, where the above constraints are encoded in singleton (global) and pair-wise potentials (smoothness (intra-layer costs) and pair-alignments (inter-layer costs)). Furthermore, we propose a novel grid-based deformation scheme, that guarantees the diffeomorphism of the deformation while being computationally favorable compared to standard deformation methods. Towards addressing important deformations we propose a compositional approach where the deformations are recovered through the sub-optimal solutions of successive discrete MRFs. The resulting paradigm is optimized using efficient linear programming. The proposed framework for the mutual deformation of the images is applied to the group-wise registration problem as well as to an atlas-based population segmentation problem. Both articially generated data with known deformations and real data of medical studies were used for the validation of the method. Promising results demonstrate the potential of our method
MRI-based classification of IDH mutation and 1p/19q codeletion status of gliomas using a 2.5D hybrid multi-task convolutional neural network
Isocitrate dehydrogenase (IDH) mutation and 1p/19q codeletion status are
important prognostic markers for glioma. Currently, they are determined using
invasive procedures. Our goal was to develop artificial intelligence-based
methods to non-invasively determine these molecular alterations from MRI. For
this purpose, pre-operative MRI scans of 2648 patients with gliomas (grade
II-IV) were collected from Washington University School of Medicine (WUSM; n =
835) and publicly available datasets viz. Brain Tumor Segmentation (BraTS; n =
378), LGG 1p/19q (n = 159), Ivy Glioblastoma Atlas Project (Ivy GAP; n = 41),
The Cancer Genome Atlas (TCGA; n = 461), and the Erasmus Glioma Database (EGD;
n = 774). A 2.5D hybrid convolutional neural network was proposed to
simultaneously localize the tumor and classify its molecular status by
leveraging imaging features from MR scans and prior knowledge features from
clinical records and tumor location. The models were tested on one internal
(TCGA) and two external (WUSM and EGD) test sets. For IDH, the best-performing
model achieved areas under the receiver operating characteristic (AUROC) of
0.925, 0.874, 0.933 and areas under the precision-recall curves (AUPRC) of
0.899, 0.702, 0.853 on the internal, WUSM, and EGD test sets, respectively. For
1p/19q, the best model achieved AUROCs of 0.782, 0.754, 0.842, and AUPRCs of
0.588, 0.713, 0.782, on those three data-splits, respectively. The high
accuracy of the model on unseen data showcases its generalization capabilities
and suggests its potential to perform a 'virtual biopsy' for tailoring
treatment planning and overall clinical management of gliomas
Benchmarking the generalizability of brain age models: Challenges posed by scanner variance and prediction bias
Machine learning has been increasingly applied to neuroimaging data to predict age, deriving a personalized biomarker with potential clinical applications. The scientific and clinical value of these models depends on their applicability to independently acquired scans from diverse sources. Accordingly, we evaluated the generalizability of two brain age models that were trained across the lifespan by applying them to three distinct early-life samples with participants aged 8-22 years. These models were chosen based on the size and diversity of their training data, but they also differed greatly in their processing methods and predictive algorithms. Specifically, one brain age model was built by applying gradient tree boosting (GTB) to extracted features of cortical thickness, surface area, and brain volume. The other model applied a 2D convolutional neural network (DBN) to minimally preprocessed slices of T1-weighted scans. Additional model variants were created to understand how generalizability changed when each model was trained with data that became more similar to the test samples in terms of age and acquisition protocols. Our results illustrated numerous trade-offs. The GTB predictions were relatively more accurate overall and yielded more reliable predictions when applied to lower quality scans. In contrast, the DBN displayed the most utility in detecting associations between brain age gaps and cognitive functioning. Broadly speaking, the largest limitations affecting generalizability were acquisition protocol differences and biased brain age estimates. If such confounds could eventually be removed without post-hoc corrections, brain age predictions may have greater utility as personalized biomarkers of healthy aging
Synthesizing pseudo-T2w images to recapture missing data in neonatal neuroimaging with applications in rs-fMRI
T1- and T2-weighted (T1w and T2w) images are essential for tissue classification and anatomical localization in Magnetic Resonance Imaging (MRI) analyses. However, these anatomical data can be challenging to acquire in non-sedated neonatal cohorts, which are prone to high amplitude movement and display lower tissue contrast than adults. As a result, one of these modalities may be missing or of such poor quality that they cannot be used for accurate image processing, resulting in subject loss. While recent literature attempts to overcome these issues in adult populations using synthetic imaging approaches, evaluation of the efficacy of these methods in pediatric populations and the impact of these techniques in conventional MR analyses has not been performed. In this work, we present two novel methods to generate pseudo-T2w images: the first is based in deep learning and expands upon previous models to 3D imaging without the requirement of paired data, the second is based in nonlinear multi-atlas registration providing a computationally lightweight alternative. We demonstrate the anatomical accuracy of pseudo-T2w images and their efficacy in existing MR processing pipelines in two independent neonatal cohorts. Critically, we show that implementing these pseudo-T2w methods in resting-state functional MRI analyses produces virtually identical functional connectivity results when compared to those resulting from T2w images, confirming their utility in infant MRI studies for salvaging otherwise lost subject data
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