14 Result(s)
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Chapter and Conference Paper
Anisotropic Fanning Aware Low-Rank Tensor Approximation Based Tractography
Low-rank higher-order tensor approximation has been used successfully to extract discrete directions for tractography from continuous fiber orientation density functions (fODFs). However, while it accounts for...
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Chapter and Conference Paper
Automated Detection of Diabetic Retinopathy from Smartphone Fundus Videos
Even though it is important to screen patients with diabetes for signs of diabetic retinopathy (DR), doing so comprehensively remains a practical challenge in low- and middle-income countries due to limited re...
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Chapter and Conference Paper
Feature Preserving Smoothing Provides Simple and Effective Data Augmentation for Medical Image Segmentation
CNNs represent the current state of the art for image classification, as well as for image segmentation. Recent work suggests that CNNs for image classification suffer from a bias towards texture, and that red...
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Chapter and Conference Paper
Better Fiber ODFs from Suboptimal Data with Autoencoder Based Regularization
We propose a novel way of estimating fiber orientation distribution functions (fODFs) from diffusion MRI. Our method combines convex optimization with unsupervised learning in a way that preserves the relative...
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Chapter and Conference Paper
A Bag-of-Features Approach to Predicting TMS Language Map** Results from DSI Tractography
Transcranial Magnetic Stimulation (TMS) can be used to indicate language-related cortex by highly focal temporary inhibition. Diffusion Spectrum Imaging (DSI) reconstructs fiber tracts that connect specific co...
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Chapter and Conference Paper
CNNs Enable Accurate and Fast Segmentation of Drusen in Optical Coherence Tomography
Optical coherence tomography (OCT) is used to diagnose and track progression of age-related macular degeneration (AMD). Drusen, which appear as bumps between Bruch’s membrane (BM) and the retinal pigment epith...
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Chapter and Conference Paper
Fast and Accurate Multi-tissue Deconvolution Using SHORE and H-psd Tensors
We propose a new regularization for spherical deconvolution in diffusion MRI. It is based on observing that higher-order tensor representations of fiber ODFs should be H-psd, i.e., they should have a positive ...
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Chapter and Conference Paper
BundleMAP: Anatomically Localized Features from dMRI for Detection of Disease
We present BundleMAP, a novel method for extracting features from diffusion MRI (dMRI), which can be used to detect disease with supervised classification. BundleMAP uses manifold learning to aggregate measure...
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Chapter and Conference Paper
Quantifying Microstructure in Fiber Crossings with Diffusional Kurtosis
Diffusional Kurtosis Imaging (DKI) is able to capture non-Gaussian diffusion and has become a popular complement to the more traditional Diffusion Tensor Imaging (DTI). In this paper, we demonstrate how strong...
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Chapter and Conference Paper
Auto-calibrating Spherical Deconvolution Based on ODF Sparsity
Spherical deconvolution models the diffusion MRI signal as the convolution of a fiber orientation density function (fODF) with a single fiber response. We propose a novel calibration procedure that automatical...
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Chapter and Conference Paper
Learning a Reliable Estimate of the Number of Fiber Directions in Diffusion MRI
Having to determine an adequate number of fiber directions is a fundamental limitation of multi-compartment models in diffusion MRI. This paper proposes a novel strategy to approach this problem, based on simu...
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Chapter and Conference Paper
Segmenting Thalamic Nuclei: What Can We Gain from HARDI?
The contrast provided by diffusion MRI has been exploited repeatedly for in vivo segmentations of thalamic nuclei. This paper systematically investigates the benefits of high-angular resolution (HARDI) data fo...
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Chapter and Conference Paper
Using Eigenvalue Derivatives for Edge Detection in DT-MRI Data
This paper introduces eigenvalue derivatives as a fundamental tool to discern the different types of edges present in matrix-valued images. It reviews basic results from perturbation theory, which allow one to...
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Chapter and Conference Paper
Flexible Segmentation and Smoothing of DT-MRI Fields Through a Customizable Structure Tensor
We present a novel structure tensor for matrix-valued images. It allows for user defined parameters that add flexibility to a number of image processing algorithms for the segmentation and smoothing of tensor ...
