Search Results for author: Adrian V. Dalca

Found 51 papers, 35 papers with code

SuperWarp: Supervised Learning and Warping on U-Net for Invariant Subvoxel-Precise Registration

no code implementations15 May 2022 Sean I. Young, Yaël Balbastre, Adrian V. Dalca, William M. Wells, Juan Eugenio Iglesias, Bruce Fischl

In recent years, learning-based image registration methods have gradually moved away from direct supervision with target warps to instead use self-supervision, with excellent results in several registration benchmarks.

Image Registration

Learning the Effect of Registration Hyperparameters with HyperMorph

no code implementations30 Mar 2022 Andrew Hoopes, Malte Hoffmann, Douglas N. Greve, Bruce Fischl, John Guttag, Adrian V. Dalca

We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values.

Image Registration

SynthStrip: Skull-Stripping for Any Brain Image

no code implementations18 Mar 2022 Andrew Hoopes, Jocelyn S. Mora, Adrian V. Dalca, Bruce Fischl, Malte Hoffmann

The removal of non-brain signal from magnetic resonance imaging (MRI) data, known as skull-stripping, is an integral component of many neuroimage analysis streams.

Skull Stripping

Computing Multiple Image Reconstructions with a Single Hypernetwork

2 code implementations22 Feb 2022 Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu

The typical approach is to train the model for a hyperparameter setting determined with some empirical or theoretical justification.

Denoising Image Reconstruction +1

SUD: Supervision by Denoising for Medical Image Segmentation

no code implementations7 Feb 2022 Sean I. Young, Adrian V. Dalca, Enzo Ferrante, Polina Golland, Bruce Fischl, Juan Eugenio Iglesias

SUD unifies temporal ensembling and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and network weight update in an optimization framework for semi-supervision.

Denoising Medical Image Segmentation +1

Hyper-Convolutions via Implicit Kernels for Medical Imaging

1 code implementation6 Feb 2022 Tianyu Ma, Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu

The key building block of a CNN is the convolutional kernel that aggregates information from the pixel neighborhood and shares weights across all pixels.

3D-StyleGAN: A Style-Based Generative Adversarial Network for Generative Modeling of Three-Dimensional Medical Images

1 code implementation20 Jul 2021 SungMin Hong, Razvan Marinescu, Adrian V. Dalca, Anna K. Bonkhoff, Martin Bretzner, Natalia S. Rost, Polina Golland

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 progression modeling.

Image Enhancement Image Generation

Unsupervised learning of MRI tissue properties using MRI physics models

no code implementations6 Jul 2021 Divya Varadarajan, Katherine L. Bouman, Andre van der Kouwe, Bruce Fischl, Adrian V. Dalca

In this work we propose an unsupervised deep-learning strategy that employs MRI physics to estimate all three tissue properties from a single multiecho MRI scan session, and generalizes across varying acquisition parameters.

Hyper-Convolution Networks for Biomedical Image Segmentation

1 code implementation IEEE/CVF Winter Conference on Applications of Computer Vision (WACV) 2022 Tianyu Ma, Adrian V. Dalca, Mert R. Sabuncu

In this paper, we propose a powerful novel building block, the hyper-convolution, which implicitly represents the convolution kernel as a function of kernel coordinates.

Semantic Segmentation

End-to-End Sequential Sampling and Reconstruction for MR Imaging

1 code implementation13 May 2021 Tianwei Yin, Zihui Wu, He Sun, Adrian V. Dalca, Yisong Yue, Katherine L. Bouman

In this paper, we leverage the sequential nature of MRI measurements, and propose a fully differentiable framework that jointly learns a sequential sampling policy simultaneously with a reconstruction strategy.

Learning to Predict with Supporting Evidence: Applications to Clinical Risk Prediction

1 code implementation4 Mar 2021 Aniruddh Raghu, John Guttag, Katherine Young, Eugene Pomerantsev, Adrian V. Dalca, Collin M. Stultz

Inference of latent variables in this model corresponds to both making a prediction and providing supporting evidence for that prediction.

Regularization-Agnostic Compressed Sensing MRI Reconstruction with Hypernetworks

2 code implementations6 Jan 2021 Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu

In this paper, we explore a novel strategy of using a hypernetwork to generate the parameters of a separate reconstruction network as a function of the regularization weight(s), resulting in a regularization-agnostic reconstruction model.

MRI Reconstruction

HyperMorph: Amortized Hyperparameter Learning for Image Registration

1 code implementation4 Jan 2021 Andrew Hoopes, Malte Hoffmann, Bruce Fischl, John Guttag, Adrian V. Dalca

We present HyperMorph, a learning-based strategy for deformable image registration that removes the need to tune important registration hyperparameters during training.

Image Registration

Predictive Modeling of Anatomy with Genetic and Clinical Data

1 code implementation9 Oct 2020 Adrian V. Dalca, Ramesh Sridharan, Mert R. Sabuncu, Polina Golland

We demonstrate prediction of follow-up anatomical scans in the ADNI cohort, and illustrate a novel analysis approach that compares a patient's scans to the predicted subject-specific healthy anatomical trajectory.

Neural Network-based Reconstruction in Compressed Sensing MRI Without Fully-sampled Training Data

1 code implementation29 Jul 2020 Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu

In this paper, we explore a novel strategy to train an unrolled reconstruction network in an unsupervised fashion by adopting a loss function widely-used in classical optimization schemes.

Joint Frequency and Image Space Learning for MRI Reconstruction and Analysis

1 code implementation2 Jul 2020 Nalini M. Singh, Juan Eugenio Iglesias, Elfar Adalsteinsson, Adrian V. Dalca, Polina Golland

This is in contrast to most current deep learning approaches for image reconstruction that treat frequency and image space features separately and often operate exclusively in one of the two spaces.

Image Denoising MRI Reconstruction

Anatomical Predictions using Subject-Specific Medical Data

no code implementations MIDL 2019 Marianne Rakic, John Guttag, Adrian V. Dalca

We present a method that predicts how a brain MRI for an individual will change over time.

An Auto-Encoder Strategy for Adaptive Image Segmentation

1 code implementation MIDL 2019 Evan M. Yu, Juan Eugenio Iglesias, Adrian V. Dalca, Mert R. Sabuncu

Thus there is a strong need for deep learning-based segmentation tools that do not require heavy supervision and can continuously adapt.

Representation Learning Semantic Segmentation

Partial Volume Segmentation of Brain MRI Scans of any Resolution and Contrast

2 code implementations21 Apr 2020 Benjamin Billot, Eleanor D. Robinson, Adrian V. Dalca, Juan Eugenio Iglesias

Partial voluming (PV) is arguably the last crucial unsolved problem in Bayesian segmentation of brain MRI with probabilistic atlases.

Cortical surface registration using unsupervised learning

1 code implementation9 Apr 2020 Jieyu Cheng, Adrian V. Dalca, Bruce Fischl, Lilla Zollei

The experiments show that the proposed SphereMorph is capable of modeling the geometric registration problem in a CNN framework and demonstrate superior registration accuracy and computational efficiency.

Learning a Probabilistic Strategy for Computational Imaging Sensor Selection

no code implementations23 Mar 2020 He Sun, Adrian V. Dalca, Katherine L. Bouman

In this paper, we demonstrate the approach in the context of a very-long-baseline-interferometry (VLBI) array design task, where sensor correlations and atmospheric noise present unique challenges.

A Learning Strategy for Contrast-agnostic MRI Segmentation

3 code implementations MIDL 2019 Benjamin Billot, Douglas Greve, Koen van Leemput, Bruce Fischl, Juan Eugenio Iglesias, Adrian V. Dalca

These samples are produced using the generative model of the classical Bayesian segmentation framework, with randomly sampled parameters for appearance, deformation, noise, and bias field.

Brain Segmentation MRI segmentation +1

ML4H Abstract Track 2019

no code implementations5 Feb 2020 Matthew B. A. McDermott, Emily Alsentzer, Sam Finlayson, Michael Oberst, Fabian Falck, Tristan Naumann, Brett K. Beaulieu-Jones, Adrian V. Dalca

A collection of the accepted abstracts for the Machine Learning for Health (ML4H) workshop at NeurIPS 2019.

Painting Many Pasts: Synthesizing Time Lapse Videos of Paintings

1 code implementation CVPR 2020 Amy Zhao, Guha Balakrishnan, Kathleen M. Lewis, Frédo Durand, John V. Guttag, Adrian V. Dalca

We present a probabilistic model that, given a single image of a completed painting, recurrently synthesizes steps of the painting process.

Omnibus Dropout for Improving The Probabilistic Classification Outputs of ConvNets

no code implementations25 Sep 2019 Zhilu Zhang, Adrian V. Dalca, Mert R. Sabuncu

Motivated by this, we explore the use of various structured dropout techniques to promote model diversity and improve the quality of probabilistic predictions.

Active Learning Ensemble Learning

Learning Conditional Deformable Templates with Convolutional Networks

1 code implementation NeurIPS 2019 Adrian V. Dalca, Marianne Rakic, John Guttag, Mert R. Sabuncu

We develop a learning framework for building deformable templates, which play a fundamental role in many image analysis and computational anatomy tasks.

Deformable Medical Image Registration Medical Image Registration

Deep-learning-based Optimization of the Under-sampling Pattern in MRI

1 code implementation26 Jul 2019 Cagla D. Bahadir, Alan Q. Wang, Adrian V. Dalca, Mert R. Sabuncu

In our experiments, we demonstrate that LOUPE-optimized under-sampling masks are data-dependent, varying significantly with the imaged anatomy, and perform well with different reconstruction methods.

Confidence Calibration for Convolutional Neural Networks Using Structured Dropout

no code implementations23 Jun 2019 Zhilu Zhang, Adrian V. Dalca, Mert R. Sabuncu

Motivated by this, we explore the use of structured dropout to promote model diversity and improve confidence calibration.

Active Learning Bayesian Inference +1

Unsupervised Deep Learning for Bayesian Brain MRI Segmentation

1 code implementation25 Apr 2019 Adrian V. Dalca, Evan Yu, Polina Golland, Bruce Fischl, Mert R. Sabuncu, Juan Eugenio Iglesias

To develop a deep learning-based segmentation model for a new image dataset (e. g., of different contrast), one usually needs to create a new labeled training dataset, which can be prohibitively expensive, or rely on suboptimal ad hoc adaptation or augmentation approaches.

Brain Image Segmentation Brain Segmentation +4

Unsupervised Data Imputation via Variational Inference of Deep Subspaces

6 code implementations8 Mar 2019 Adrian V. Dalca, John Guttag, Mert R. Sabuncu

In this work, we introduce a general probabilistic model that describes sparse high dimensional imaging data as being generated by a deep non-linear embedding.

Imputation Variational Inference

Unsupervised Learning of Probabilistic Diffeomorphic Registration for Images and Surfaces

1 code implementation8 Mar 2019 Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu

We present a probabilistic generative model and derive an unsupervised learning-based inference algorithm that uses insights from classical registration methods and makes use of recent developments in convolutional neural networks (CNNs).

Constrained Diffeomorphic Image Registration Deformable Medical Image Registration +2

Anatomical Priors in Convolutional Networks for Unsupervised Biomedical Segmentation

2 code implementations CVPR 2018 Adrian V. Dalca, John Guttag, Mert R. Sabuncu

The integration of anatomical priors can facilitate CNN-based anatomical segmentation in a range of novel clinical problems, where few or no annotations are available and thus standard networks are not trainable.

MRI segmentation

Learning-based Optimization of the Under-sampling Pattern in MRI

1 code implementation7 Jan 2019 Cagla Deniz Bahadir, Adrian V. Dalca, Mert R. Sabuncu

Acquisition of Magnetic Resonance Imaging (MRI) scans can be accelerated by under-sampling in k-space (i. e., the Fourier domain).

Fast Learning-based Registration of Sparse 3D Clinical Images

no code implementations17 Dec 2018 Kathleen M. Lewis, Natalia S. Rost, John Guttag, Adrian V. Dalca

We present a learning-based registration method, SparseVM, that is more accurate and orders of magnitude faster than the most accurate clinical registration methods.

Image Registration Registration Of Sparse Clinical Images

Gaussian Process Prior Variational Autoencoders

2 code implementations NeurIPS 2018 Francesco Paolo Casale, Adrian V. Dalca, Luca Saglietti, Jennifer Listgarten, Nicolo Fusi

In this work, we introduce a new model, the Gaussian Process (GP) Prior Variational Autoencoder (GPPVAE), to specifically address this issue.

Time Series

VoxelMorph: A Learning Framework for Deformable Medical Image Registration

6 code implementations14 Sep 2018 Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca

In contrast to this approach, and building on recent learning-based methods, we formulate registration as a function that maps an input image pair to a deformation field that aligns these images.

Deformable Medical Image Registration Diffeomorphic Medical Image Registration +1

Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease

no code implementations11 Sep 2018 Danielle F. Pace, Adrian V. Dalca, Tom Brosch, Tal Geva, Andrew J. Powell, Jürgen Weese, Mehdi H. Moghari, Polina Golland

We demonstrate the advantages of this approach on a dataset of 20 images from CHD patients, learning a model that accurately segments individual heart chambers and great vessels.

Medical Image Imputation from Image Collections

2 code implementations17 Aug 2018 Adrian V. Dalca, Katherine L. Bouman, William T. Freeman, Natalia S. Rost, Mert R. Sabuncu, Polina Golland

We present an algorithm for creating high resolution anatomically plausible images consistent with acquired clinical brain MRI scans with large inter-slice spacing.

Image Imputation Imputation +1

Unsupervised Learning for Fast Probabilistic Diffeomorphic Registration

2 code implementations11 May 2018 Adrian V. Dalca, Guha Balakrishnan, John Guttag, Mert R. Sabuncu

We demonstrate our method on a 3D brain registration task, and provide an empirical analysis of the algorithm.

Synthesizing Images of Humans in Unseen Poses

1 code implementation CVPR 2018 Guha Balakrishnan, Amy Zhao, Adrian V. Dalca, Fredo Durand, John Guttag

Given an image of a person and a desired pose, we produce a depiction of that person in that pose, retaining the appearance of both the person and background.

Image Generation

Reconstructing Video from Interferometric Measurements of Time-Varying Sources

1 code implementation3 Nov 2017 Katherine L. Bouman, Michael D. Johnson, Adrian V. Dalca, Andrew A. Chael, Freek Roelofs, Sheperd S. Doeleman, William T. Freeman

Most recently, the Event Horizon Telescope (EHT) has extended VLBI to short millimeter wavelengths with a goal of achieving angular resolution sufficient for imaging the event horizons of nearby supermassive black holes.

Image Imputation Radio Interferometry

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