272 research outputs found
Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction.
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome
Shallow vs deep learning architectures for white matter lesion segmentation in the early stages of multiple sclerosis
In this work, we present a comparison of a shallow and a deep learning
architecture for the automated segmentation of white matter lesions in MR
images of multiple sclerosis patients. In particular, we train and test both
methods on early stage disease patients, to verify their performance in
challenging conditions, more similar to a clinical setting than what is
typically provided in multiple sclerosis segmentation challenges. Furthermore,
we evaluate a prototype naive combination of the two methods, which refines the
final segmentation. All methods were trained on 32 patients, and the evaluation
was performed on a pure test set of 73 cases. Results show low lesion-wise
false positives (30%) for the deep learning architecture, whereas the shallow
architecture yields the best Dice coefficient (63%) and volume difference
(19%). Combining both shallow and deep architectures further improves the
lesion-wise metrics (69% and 26% lesion-wise true and false positive rate,
respectively).Comment: Accepted to the MICCAI 2018 Brain Lesion (BrainLes) worksho
On the Laplace–Beltrami Operator and Brain Surface Flattening
©1999 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or distribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.DOI: 10.1109/42.796283In this paper, using certain conformal mappings from uniformization theory, the authors give an explicit method for flattening the brain surface in a way which preserves angles. From a triangulated surface representation of the cortex, the authors indicate how the procedure may be implemented using finite elements. Further, they show how the geometry of the brain surface may be studied using this approach
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Genetic contributions to changes of fiber tracts of ventral visual stream in 22q11.2 deletion syndrome
Patients with 22q11.2 deletion syndrome (22q11.2DS) represent a population at high risk for
developing schizophrenia, as well as learning disabilities. Deficits in visuo-spatial memory are
thought to underlie some of the cognitive disabilities. Neuronal substrates of visuo-spatial memory include the inferior fronto-occipital fasciculus (IFOF) and the inferior longitudinal fasciculus (ILF), two tracts that comprise the ventral visual stream. Diffusion Tensor Magnetic Resonance Imaging (DT-MRI) is an established method to evaluate white matter (WM) connections in vivo. DT-MRI scans of nine 22q11.2DS young adults and nine matched healthy subjects were acquired. Tractography of the IFOF and the ILF was performed. DT-MRI indices, including Fractional anisotropy (FA) (measure of WM changes), axial diffusivity (AD, measure of axonal changes) and radial diffusivity (RD, measure of myelin changes) of each of the tracts and each group were measured and compared. The 22q11.2DS group showed statistically significant reductions of FA in IFOF in the left hemisphere. Additionally, reductions of AD were found in the IFOF and the ILF in both hemispheres. These findings might be the consequence of axonal changes, which is possibly due to fewer, thinner, or less organized fibers. No changes in RD were detected in any of the tracts delineated, which is in contrast to findings in schizophrenia patients where increases in RD are believed to be indicative of demyelination. We conclude that reduced axonal changes may be key to understanding the underlying pathology of WM leading to the visuo-spatial phenotype in 22q11.2DS
Gray matter volume reduction in rostral middle frontal gyrus in patients with chronic schizophrenia
The dorsolateral prefrontal cortex (DLPFC) is a brain region that has figured prominently in studies of schizophrenia and working memory, yet the exact neuroanatomical localization of this brain region remains to be defined. DLPFC primarily involves the superior frontal gyrus and middle frontal gyrus (MFG). The latter, however is not a single neuroanatomical entity but instead is comprised of rostral (anterior, middle, and posterior) and caudal regions. In this study we used structural MRI to develop a method for parcellating MFG into its component parts. We focused on this region of DLPFC because it includes BA46, a region involved in working memory. We evaluated volume differences in MFG in 20 patients with chronic schizophrenia and 20 healthy controls. Mid-rostral MFG (MR-MFG) was delineated within the rostral MFG using anterior and posterior neuroanatomical landmarks derived from cytoarchitectonic definitions of BA46. Gray matter volumes of MR-MFG were then compared between groups, and a significant reduction in gray matter volume was observed (p b 0.008), but not in other areas of MFG (i.e., anterior or posterior rostral MFG, or caudal regions of MFG). Our results demonstrate that volumetric alterations in MFG gray matter are localized exclusively to MR-MFG. 3D reconstructions of the cortical surface made it possible to follow MFG into its anterior part, where other approaches have failed. This method of parcellation offers a more precise way of measuring MR-MFG that will likely be important in further documentation of DLPFC anomalies in schizophrenia
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Neuroimaging of structural pathology and connectomics in traumatic brain injury: Toward personalized outcome prediction☆
Recent contributions to the body of knowledge on traumatic brain injury (TBI) favor the view that multimodal neuroimaging using structural and functional magnetic resonance imaging (MRI and fMRI, respectively) as well as diffusion tensor imaging (DTI) has excellent potential to identify novel biomarkers and predictors of TBI outcome. This is particularly the case when such methods are appropriately combined with volumetric/morphometric analysis of brain structures and with the exploration of TBI-related changes in brain network properties at the level of the connectome. In this context, our present review summarizes recent developments on the roles of these two techniques in the search for novel structural neuroimaging biomarkers that have TBI outcome prognostication value. The themes being explored cover notable trends in this area of research, including (1) the role of advanced MRI processing methods in the analysis of structural pathology, (2) the use of brain connectomics and network analysis to identify outcome biomarkers, and (3) the application of multivariate statistics to predict outcome using neuroimaging metrics. The goal of the review is to draw the community's attention to these recent advances on TBI outcome prediction methods and to encourage the development of new methodologies whereby structural neuroimaging can be used to identify biomarkers of TBI outcome
The NCI Imaging Data Commons as a platform for reproducible research in computational pathology
Background and Objectives: Reproducibility is a major challenge in developing
machine learning (ML)-based solutions in computational pathology (CompPath).
The NCI Imaging Data Commons (IDC) provides >120 cancer image collections
according to the FAIR principles and is designed to be used with cloud ML
services. Here, we explore its potential to facilitate reproducibility in
CompPath research.
Methods: Using the IDC, we implemented two experiments in which a
representative ML-based method for classifying lung tumor tissue was trained
and/or evaluated on different datasets. To assess reproducibility, the
experiments were run multiple times with separate but identically configured
instances of common ML services.
Results: The AUC values of different runs of the same experiment were
generally consistent. However, we observed small variations in AUC values of up
to 0.045, indicating a practical limit to reproducibility.
Conclusions: We conclude that the IDC facilitates approaching the
reproducibility limit of CompPath research (i) by enabling researchers to reuse
exactly the same datasets and (ii) by integrating with cloud ML services so
that experiments can be run in identically configured computing environments.Comment: 13 pages, 5 figures; improved manuscript, new experiments with P100
GP
A semi-supervised large margin algorithm for white matter hyperintensity segmentation
Precise detection and quantification of white matter hyperintensities (WMH) is of great interest in studies of neurodegenerative diseases (NDs). In this work, we propose a novel semi-supervised large margin algorithm for the segmentation of WMH. The proposed algorithm optimizes a kernel based max-margin objective function which aims to maximize the margin averaged over inliers and outliers while exploiting a limited amount of available labelled data. We show that the learning problem can be formulated as a joint framework learning a classifier and a label assignment simultaneously, which can be solved efficiently by an iterative algorithm. We evaluate our method on a database of 280 brain Magnetic Resonance (MR) images from subjects that either suffered from subjective memory complaints or were diagnosed with NDs. The segmented WMH volumes correlate well with the standard clinical measurement (Fazekas score), and both the qualitative visualization results and quantitative correlation scores of the proposed algorithm outperform other well known methods for WMH segmentation
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