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Article
Open AccessGenomics of perivascular space burden unravels early mechanisms of cerebral small vessel disease
Perivascular space (PVS) burden is an emerging, poorly understood, magnetic resonance imaging marker of cerebral small vessel disease, a leading cause of stroke and dementia. Genome-wide association studies in...
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Chapter and Conference Paper
Automated Estimation of the Spinal Curvature via Spine Centerline Extraction with Ensembles of Cascaded Neural Networks
Scoliosis is a condition defined by an abnormal spinal curvature. For diagnosis and treatment planning of scoliosis, spinal curvature can be estimated using Cobb angles. We propose an automated method for the ...
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Chapter and Conference Paper
Event-Based Modeling with High-Dimensional Imaging Biomarkers for Estimating Spatial Progression of Dementia
Event-based models (EBM) are a class of disease progression models that can be used to estimate temporal ordering of neuropathological changes from cross-sectional data. Current EBMs only handle scalar biomark...
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Chapter and Conference Paper
Automated Lesion Detection by Regressing Intensity-Based Distance with a Neural Network
Localization of focal vascular lesions on brain MRI is an important component of research on the etiology of neurological disorders. However, manual annotation of lesions can be challenging, time-consuming an...
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Chapter and Conference Paper
Automated Quantification of Enlarged Perivascular Spaces in Clinical Brain MRI Across Sites
Enlarged perivascular spaces (PVS) are structural brain changes visible in MRI, and are a marker of cerebral small vessel disease. Most studies use time-consuming and subjective visual scoring to assess these ...
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Chapter and Conference Paper
Patient-Specific Conditional Joint Models of Shape, Image Features and Clinical Indicators
We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis. The key idea is to employ a copula model to sep...
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Chapter and Conference Paper
Hydranet: Data Augmentation for Regression Neural Networks
Deep learning techniques are often criticized to heavily depend on a large quantity of labeled data. This problem is even more challenging in medical image analysis where the annotator expertise is often scar...
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Chapter and Conference Paper
APIR-Net: Autocalibrated Parallel Imaging Reconstruction Using a Neural Network
Deep learning has been successfully demonstrated in MRI reconstruction of accelerated acquisitions. However, its dependence on representative training data limits the application across different contrasts, an...
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Chapter and Conference Paper
Semi-supervised Medical Image Segmentation via Learning Consistency Under Transformations
The scarcity of labeled data often limits the application of supervised deep learning techniques for medical image segmentation. This has motivated the development of semi-supervised techniques that learn from...
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Chapter and Conference Paper
Deep Learning from Label Proportions for Emphysema Quantification
We propose an end-to-end deep learning method that learns to estimate emphysema extent from proportions of the diseased tissue. These proportions were visually estimated by experts using a standard grading sys...
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Chapter and Conference Paper
Segmentation of Intracranial Arterial Calcification with Deeply Supervised Residual Dropout Networks
Intracranial carotid artery calcification (ICAC) is a major risk factor for stroke, and might contribute to dementia and cognitive decline. Reliance on time-consuming manual annotation of ICAC hampers much dem...
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Chapter and Conference Paper
GP-Unet: Lesion Detection from Weak Labels with a 3D Regression Network
We propose a novel convolutional neural network for lesion detection from weak labels. Only a single, global label per image - the lesion count - is needed for training. We train a regression network with a fu...
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Chapter and Conference Paper
Hands-Free Segmentation of Medical Volumes via Binary Inputs
We propose a novel hands-free method to interactively segment 3D medical volumes. In our scenario, a human user progressively segments an organ by answering a series of questions of the form “Is this voxel inside...
