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Automatic MR whole prostate segmentation is a challenging task. Recent approaches have attempted to harness the capabilities of deep learning for MR prostate segmentation to tackle pixel-level labeling tasks. Patch-based and hierarchical features-based deep CNN models were used to delineate the prostate boundary. To further investigate this problem, we introduce a Holistically-Nested Edge Detector...
White matter segmentation is an essential step to study whole-brain structural connectivity via diffusion MRI white matter tractography. One important goal of segmentation methods is to improve consistency of the white matter segmentations across multiple subjects. In this study, we quantitatively compare two popular white matter segmentation strategies, i.e., a cortical-parcellation-based method...
Accurate registration plays a critical role in group-wise functional Magnetic Resonance Imaging (fMRI) image analysis, as spatial correspondence among different brain images is a prerequisite for inferring meaningful patterns. However, the problem is challenging and remains open, and more effort should be made to advance the state-of-the-art image registration methods for fMRI images. Inspired by...
Non-local means (NLM) filtering of fMRI can reduce noise while preserving spatial structure. We have developed a variant called temporal-NLM (tNLM) which uses similarity in time-series between voxels as the basis for computing the weights in the filter. Using tNLM, dynamic fMRI data can be denoised while spatial boundaries between functionally distinct areas in the brain tend to be preserved. The...
The differential diagnosis among ADHD subtypes is an important research area for the neuroimaging community. We pursue this goal by using machine learning techniques in this study. Selective subjects matched by age and handedness information from publicly available ADHD-200 dataset were used in this study. In addition, this work is based only on the resting-state fMRI images. We calculated the global...
In this paper, we propose an end-to-end trainable Convolutional Neural Network (CNN) architecture called the M-net, for segmenting deep (human) brain structures from Magnetic Resonance Images (MRI). A novel scheme is used to learn to combine and represent 3D context information of a given slice in a 2D slice. Consequently, the M-net utilizes only 2D convolution though it operates on 3D data, which...
High-dimensional signals, including dynamic magnetic resonance (dMR) images, often lie on low dimensional manifold. While many current dynamic magnetic resonance imaging (dMRI) reconstruction methods rely on priors which promote low-rank and sparsity, this paper proposes a novel manifold-based framework, we term M-MRI, for dMRI reconstruction from highly undersampled k-space data. Images in dMRI are...
In this paper we address the problem of bone segmentation in MRI images of children, in the region of the pelvis. To cope with the complex structure of the bones in this region and their changing topology during growth, we propose a method relying on 3D bone templates. These models are built from 3D CT images. For a given MRI volume, the closest template is chosen and registered on the MRI data. This...
Brain development is a protracted and dynamic process. Many studies have charted the trajectory of white matter development, but here we sought to map these effects in greater detail, based on a large set of fiber tracts automatically extracted from HARDI (high angular resolution diffusion imaging) at 4 tesla. We used autoMATE (automated multi-atlas tract extraction) to extract diffusivity measures...
Accurate segmentation of hip joint cartilage from magnetic resonance (MR) images provides a basis for obtaining morphometric data of articular cartilages for investigation of pathoanatomical conditions such as osteoarthritis. In this paper, we present an automated MR-based cartilage segmentation method using an ensemble of neural networks for the individual femoral and acetabular cartilage plates...
We introduce a new approach for the elastic registration of high-resolution 3D polarized light imaging (3D PLI) data of histological sections of the human brain. For accurate registration of different types of 3D PLI modalities, we propose a novel intensity similarity measure that is based on a least-squares formulation of normalized cross-correlation. Moreover, we present a fully automatic registration...
For electrophysiology procedures, obtaining the information of scar within the left ventricle is very important for diagnosis, therapy planning and patient prognosis. The clinical gold standard to visualize scar is late-gadolinium-enhanced-MRI (LGE-MRI). The viability assessment of the myocardium often requires the prior segmentation of the left ventricle (LV). To overcome this problem, we propose...
This paper proposes a multi-shell sampling scheme and corresponding transforms for the accurate reconstruction of the diffusion signal in diffusion MRI by expansion in the spherical polar Fourier (SPF) basis. The sampling scheme uses an optimal number of samples, equal to the degrees of freedom of the band-limited diffusion signal in the SPF domain, and allows for computationally efficient reconstruction...
Segmentation of the developing cortical plate from MRI data of the post-mortem fetal brain is highly challenging due to partial volume effects, low contrast, and heterogeneous maturation caused by ongoing myelination processes. We present a new atlas-free method that segments the inner and outer boundaries of the cortical plate in fetal brains by exploiting diffusion-weighted imaging cues and using...
We consider the problem of domain shift in analyses of brain MRI data. While many different datasets are publicly available, most algorithms are still trained on a single dataset and often suffer the problem of limited and unbalanced sample sizes. In this work, we propose a surprisingly simple strategy to reduce the impact of domain shift - caused by different data sources and processing pipelines...
This paper introduces a comprehensive computer-aided diagnosis (CAD) system for autism diagnosis that integrates anatomical and functional information of the brain using both structural and functional magnetic resonance (MR) brain images. In order to move towards the idea of personalized medicine, analysis of the brain's Brodmann areas (BAs) is conducted to reach a diagnosis decision on the local...
Registration of diffusion weighted datasets remains a challenging task in the process of quantifying diffusion indexes. Respiratory and cardiac motion, as well as echo-planar characteristic geometric distortions, may greatly limit accuracy on parameter estimation, specially for the liver. This work proposes a methodology for the non-rigid registration of multiparametric abdominal diffusion weighted...
This paper presents a novel longitudinal framework for clinical score prediction in Alzheimer's disease (AD) diagnosis. In contrast to the previous approaches that use the data collected at a single time point only for the clinical score prediction, we propose to exploit the imaging data of multiple time points. Furthermore, a spatial-temporal group sparse method is proposed for robust feature selection...
An accurate registration plays a critical role in group-wise fMRI image analysis. Inspired by the observations that common functional networks can be reconstructed from fMRI image across individuals and in different brain states, we propose a novel computational framework for fMRI image registration by using these common function networks as references for correspondence between individuals. This...
In recent years, magnetic resonance imaging (MRI) has been explored for non-invasive assessment of renal transplant function. This paper proposes a computer-aided diagnostic (CAD) system for the assessment of renal transplant status, which integrates both clinical and MRI-derived biomarkers. The latter are derived from either 3D (2D + time) dynamic contrast-enhanced MRI or 4D (3D + b-value) diffusion-weighted...
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