Chapter and Conference Paper
We tackle classification based on brain connectivity derived from diffusion magnetic resonance images. We propose a machine-learning model inspired by graph convolutional networks (GCNs), which takes a brain-c...
Chapter and Conference Paper
Most state-of-the-art techniques for medical image segmentation rely on deep-learning models. These models, however, are often trained on narrowly-defined tasks in a supervised fashion, which requires expensi...
Chapter and Conference Paper
In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to self-supervision using segmentations, producing promising results across severa...
Article
Acute ischemic stroke affects men and women differently. In particular, women are often reported to experience higher acute stroke severity than men. We derived a low-dimensional representation of anatomical s...
Chapter and Conference Paper
Image synthesis via Generative Adversarial Networks (GANs) of three-dimensional (3D) medical images has great potential that can be extended to many medical applications, such as, image enhancement and disease...
Chapter and Conference Paper
Reconstructing under-sampled k-space measurements in Compressed Sensing MRI (CS-MRI) is classically solved by minimizing a regularized least-squares cost function. In the absence of fully-sampled training data, t...
Chapter and Conference Paper
We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training. Classical registration methods solve an ...
Chapter and Conference Paper
Compressed Sensing MRI (CS-MRI) has shown promise in reconstructing under-sampled MR images, offering the potential to reduce scan times. Classical techniques minimize a regularized least-squares cost function...
Chapter and Conference Paper
Due to the dynamic cortical development of neonates after birth, existing cortical surface atlases for adults are not suitable for representing neonatal brains. It has been proposed that pediatric spatio-tempo...
Chapter and Conference Paper
A brain template that describes the anatomical layout of an “average” brain is an essential building block of neuroimage analysis pipelines. However, a single template is often not sufficient to fully capture ...
Chapter and Conference Paper
Neuroimaging to neuropathology correlation (NTNC) promis-es to enable the transfer of microscopic signatures of pathology to in vivo imaging with MRI, ultimately enhancing clinical care. NTNC traditionally requir...
Chapter and Conference Paper
Partial voluming (PV) is arguably the last crucial unsolved problem in Bayesian segmentation of brain MRI with probabilistic atlases. PV occurs when voxels contain multiple tissue classes, giving rise to image...
Chapter and Conference Paper
Probabilistic atlas priors have been commonly used to derive adaptive and robust brain MRI segmentation algorithms. Widely-used neuroimage analysis pipelines rely heavily on these techniques, which are often c...
Chapter and Conference Paper
Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated by under-sampling in k-space (i.e., the Fourier domain). In this paper, we consider the problem of optimizing the sub-sampling pattern i...
Chapter and Conference Paper
Traditional deformable registration techniques achieve impressive results and offer a rigorous theoretical treatment, but are computationally intensive since they solve an optimization problem for each image p...
Book and Conference Proceedings
Second International Workshop, GRAIL 2018 and First International Workshop, Beyond MIC 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings
Chapter and Conference Paper
We propose a new iterative segmentation model which can be accurately learned from a small dataset. A common approach is to train a model to directly segment an image, requiring a large collection of manually ...
Book and Conference Proceedings
First International Workshop, GRAIL 2017, 6th International Workshop, MFCA 2017, and Third International Workshop, MICGen 2017, Held in Conjunction with MICCAI 2017, Québec City, QC, Canada, September 10–14, 2017, Proceedings
Chapter and Conference Paper
This paper presents an efficient algorithm for large deformation diffeomorphic metric map** (LDDMM) with geodesic shooting for image registration. We introduce a novel finite dimensional Fourier representati...
Chapter and Conference Paper
We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing. Although large databases of clinical images ...
