75 research outputs found
A pipeline for the analysis of 18F-FDG PET data on the cortical surface and its evaluation on ADNI
International audienc
Automatic Tissue Segmentation with Deep Learning in Patients with Congenital or Acquired Distortion of Brain Anatomy
Brains with complex distortion of cerebral anatomy present several challenges
to automatic tissue segmentation methods of T1-weighted MR images. First, the
very high variability in the morphology of the tissues can be incompatible with
the prior knowledge embedded within the algorithms. Second, the availability of
MR images of distorted brains is very scarce, so the methods in the literature
have not addressed such cases so far. In this work, we present the first
evaluation of state-of-the-art automatic tissue segmentation pipelines on
T1-weighted images of brains with different severity of congenital or acquired
brain distortion. We compare traditional pipelines and a deep learning model,
i.e. a 3D U-Net trained on normal-appearing brains. Unsurprisingly, traditional
pipelines completely fail to segment the tissues with strong anatomical
distortion. Surprisingly, the 3D U-Net provides useful segmentations that can
be a valuable starting point for manual refinement by
experts/neuroradiologists
Simultaneous lesion and neuroanatomy segmentation in Multiple Sclerosis using deep neural networks
Segmentation of both white matter lesions and deep grey matter structures is
an important task in the quantification of magnetic resonance imaging in
multiple sclerosis. Typically these tasks are performed separately: in this
paper we present a single segmentation solution based on convolutional neural
networks (CNNs) for providing fast, reliable segmentations of multimodal
magnetic resonance images into lesion classes and normal-appearing grey- and
white-matter structures. We show substantial, statistically significant
improvements in both Dice coefficient and in lesion-wise specificity and
sensitivity, compared to previous approaches, and agreement with individual
human raters in the range of human inter-rater variability. The method is
trained on data gathered from a single centre: nonetheless, it performs well on
data from centres, scanners and field-strengths not represented in the training
dataset. A retrospective study found that the classifier successfully
identified lesions missed by the human raters.
Lesion labels were provided by human raters, while weak labels for other
brain structures (including CSF, cortical grey matter, cortical white matter,
cerebellum, amygdala, hippocampus, subcortical GM structures and choroid
plexus) were provided by Freesurfer 5.3. The segmentations of these structures
compared well, not only with Freesurfer 5.3, but also with FSL-First and
Freesurfer 6.0
Context Matters: Graph-based Self-supervised Representation Learning for Medical Images
Supervised learning method requires a large volume of annotated datasets.
Collecting such datasets is time-consuming and expensive. Until now, very few
annotated COVID-19 imaging datasets are available. Although self-supervised
learning enables us to bootstrap the training by exploiting unlabeled data, the
generic self-supervised methods for natural images do not sufficiently
incorporate the context. For medical images, a desirable method should be
sensitive enough to detect deviation from normal-appearing tissue of each
anatomical region; here, anatomy is the context. We introduce a novel approach
with two levels of self-supervised representation learning objectives: one on
the regional anatomical level and another on the patient-level. We use graph
neural networks to incorporate the relationship between different anatomical
regions. The structure of the graph is informed by anatomical correspondences
between each patient and an anatomical atlas. In addition, the graph
representation has the advantage of handling any arbitrarily sized image in
full resolution. Experiments on large-scale Computer Tomography (CT) datasets
of lung images show that our approach compares favorably to baseline methods
that do not account for the context. We use the learnt embedding to quantify
the clinical progression of COVID-19 and show that our method generalizes well
to COVID-19 patients from different hospitals. Qualitative results suggest that
our model can identify clinically relevant regions in the images.Comment: Accepted to AAAI 202
Effect of Socioeconomic Status (SES) Disparity on Neural Development in Female African-American Infants at 1 Month
There is increasing interest in both the cumulative and long term impact of early life adversity on brain structure and function, especially as the brain is both highly vulnerable and highly adaptive during childhood. Relationships between SES and neural development have been shown in children older than age two years. Less is known regarding the impact of SES on neural development in children before age two. This paper examines the effect of SES, indexed by income-to-needs (ITN) and maternal education, on cortical, deep gray, and white matter volumes in term, healthy, appropriate for gestational age, African American, female infants. At 44-46 post-conception weeks, unsedated infants underwent MRI (3.0T Siemens Verio scanner, 32-channel head coil). Images were segmented based on a locally-constructed template. Utilizing hierarchical linear regression, overall and component (maternal education and ITN) SES effects on MRI volumes were examined. In this cohort of healthy African American infants of varying SES, lower SES was associated with smaller cortical gray and deep gray matter volumes. These SES effects on neural outcome at such a young age build on similar studies of older children, suggesting that the biological embedding of adversity may occur very early in development
Neural Connectivity Evidence for a Categorical-Dimensional Hybrid Model of Autism Spectrum Disorder
Autism spectrum disorder (ASD) encompasses a complex presentation of symptoms that include deficits in social interaction and repetitive or stereotyped interests/behaviors. In keeping with the increasing recognition of both the dimensional characteristics of ASD symptoms and the categorical nature of a diagnosis, we sought to delineate their neural mechanisms based on the functional connectivity of four known neural networks (i.e., the default-mode network, the dorsal attention network, the salience network, and the executive control network)
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Microstructural Alterations in Hippocampal Subfields Mediate Age-Related Memory Decline in Humans.
Aging, even in the absence of clear pathology of dementia, is associated with cognitive decline. Neuroimaging, especially diffusion-weighted imaging, has been highly valuable in understanding some of these changes in live humans, non-invasively. Traditional tensor techniques have revealed that the integrity of the fornix and other white matter tracts significantly deteriorates with age, and that this deterioration is highly correlated with worsening cognitive performance. However, traditional tensor techniques are still not specific enough to indict explicit microstructural features that may be responsible for age-related cognitive decline and cannot be used to effectively study gray matter properties. Here, we sought to determine whether recent advances in diffusion-weighted imaging, including Neurite Orientation Dispersion and Density Imaging (NODDI) and Constrained Spherical Deconvolution, would provide more sensitive measures of age-related changes in the microstructure of the medial temporal lobe. We evaluated these measures in a group of young (ages 20-38 years old) and older (ages 59-84 years old) adults and assessed their relationships with performance on tests of cognition. We found that the fiber density (FD) of the fornix and the neurite density index (NDI) of the fornix, hippocampal subfields (DG/CA3, CA1, and subiculum), and parahippocampal cortex, varied as a function of age in a cross-sectional cohort. Moreover, in the fornix, DG/CA3, and CA1, these changes correlated with memory performance on the Rey Auditory Verbal Learning Test (RAVLT), even after regressing out the effect of age, suggesting that they were capturing neurobiological properties directly related to performance in this task. These measures provide more details regarding age-related neurobiological properties. For example, a change in fiber density could mean a reduction in axonal packing density or myelination, and the increase in NDI observed might be explained by changes in dendritic complexity or even sprouting. These results provide a far more comprehensive view than previously determined on the possible system-wide processes that may be occurring because of healthy aging and demonstrate that advanced diffusion-weighted imaging is evolving into a powerful tool to study more than just white matter properties
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