no code implementations • 26 Feb 2024 • William Kelley, Nathan Ngo, Adrian V. Dalca, Bruce Fischl, Lilla Zöllei, Malte Hoffmann
With the emergence of multi-institutional pediatric data acquisition efforts to broaden the understanding of perinatal brain development, it is essential to develop robust and well-tested tools ready for the relevant data processing.
1 code implementation • 24 Jan 2024 • Marianne Rakic, Hallee E. Wong, Jose Javier Gonzalez Ortiz, Beth Cimini, John Guttag, Adrian V. Dalca
Existing learning-based solutions to medical image segmentation have two important shortcomings.
1 code implementation • 12 Dec 2023 • Hallee E. Wong, Marianne Rakic, John Guttag, Adrian V. Dalca
These include a training strategy that incorporates both a highly diverse set of images and tasks, novel algorithms for simulated user interactions and labels, and a network that enables fast inference.
no code implementations • 22 Oct 2023 • Jian Li, Greta Tuckute, Evelina Fedorenko, Brian L. Edlow, Adrian V. Dalca, Bruce Fischl
By recognizing the mismatch between geometry and function, JOSA provides new insights into the future development of registration methods using joint analysis of the brain structure and function.
1 code implementation • 21 Jul 2023 • Kathleen M. Lewis, Emily Mu, Adrian V. Dalca, John Guttag
We demonstrate the utility of GIST by fine-tuning vision-language models on the image-and-generated-text pairs to learn an aligned vision-language representation space for improved classification.
Fine-Grained Image Classification Image-text Classification +4
1 code implementation • 13 Jul 2023 • Neel Dey, S. Mazdak Abulnaga, Benjamin Billot, Esra Abaci Turk, P. Ellen Grant, Adrian V. Dalca, Polina Golland
Star-convex shapes arise across bio-microscopy and radiology in the form of nuclei, nodules, metastases, and other units.
no code implementations • 6 Jul 2023 • Heejong Kim, Victor Ion Butoi, Adrian V. Dalca, Daniel J. A. Margolis, Mert R. Sabuncu
Most state-of-the-art techniques for medical image segmentation rely on deep-learning models.
1 code implementation • CVPR 2023 • Steffen Czolbe, Adrian V. Dalca
We introduce Neuralizer, a single model that generalizes to previously unseen neuroimaging tasks and modalities without the need for re-training or fine-tuning.
no code implementations • 2 May 2023 • Anees Kazi, Jocelyn Mora, Bruce Fischl, Adrian V. Dalca, Iman Aganj
To test the ability of our model to extract complementary and representative features from brain connectivity data, we chose the task of sex classification.
no code implementations • 25 Apr 2023 • Zihui Wu, Tianwei Yin, Yu Sun, Robert Frost, Andre van der Kouwe, Adrian V. Dalca, Katherine L. Bouman
Traditional CS-MRI methods often separately address measurement subsampling, image reconstruction, and task prediction, resulting in a suboptimal end-to-end performance.
2 code implementations • 19 Apr 2023 • Alan Q. Wang, Evan M. Yu, Adrian V. Dalca, Mert R. Sabuncu
Our core insight which addresses these shortcomings is that corresponding keypoints between images can be used to obtain the optimal transformation via a differentiable closed-form expression.
1 code implementation • ICCV 2023 • Victor Ion Butoi, Jose Javier Gonzalez Ortiz, Tianyu Ma, Mert R. Sabuncu, John Guttag, Adrian V. Dalca
We present UniverSeg, a method for solving unseen medical segmentation tasks without additional training.
no code implementations • 2 Mar 2023 • Jian Li, Greta Tuckute, Evelina Fedorenko, Brian L. Edlow, Bruce Fischl, Adrian V. Dalca
Brain surface-based image registration, an important component of brain image analysis, establishes spatial correspondence between cortical surfaces.
no code implementations • 26 Jan 2023 • Malte Hoffmann, Andrew Hoopes, Douglas N. Greve, Bruce Fischl, Adrian V. Dalca
Most affine methods are agnostic to anatomy, meaning the registration will be inaccurate if algorithms consider all structures in the image.
1 code implementation • 25 Jan 2023 • Nalini M. Singh, Neel Dey, Malte Hoffmann, Bruce Fischl, Elfar Adalsteinsson, Robert Frost, Adrian V. Dalca, Polina Golland
Motion artifacts are a pervasive problem in MRI, leading to misdiagnosis or mischaracterization in population-level imaging studies.
no code implementations • 15 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.
no code implementations • 30 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.
no code implementations • 18 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.
2 code implementations • 22 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.
no code implementations • 7 Feb 2022 • Sean I. Young, Adrian V. Dalca, Enzo Ferrante, Polina Golland, Christopher A. Metzler, Bruce Fischl, Juan Eugenio Iglesias
SUD unifies stochastic averaging and spatial denoising techniques under a spatio-temporal denoising framework and alternates denoising and model weight update steps in an optimization framework for semi-supervision.
1 code implementation • 6 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.
no code implementations • 13 Dec 2021 • Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari, Michel Bilello, Benedikt Wiestler, Julian Schwarting, Evan Calabrese, Jeffrey Rudie, Syed Abidi, Mina Mousa, Javier Villanueva-Meyer, Brandon K. K. Fields, Florian Kofler, Russell Takeshi Shinohara, Juan Eugenio Iglesias, Tony C. W. Mok, Albert C. S. Chung, Marek Wodzinski, Artur Jurgas, Niccolo Marini, Manfredo Atzori, Henning Muller, Christoph Grobroehmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich, Luca Canalini, Jan Klein, Annika Gerken, Stefan Heldmann, Alessa Hering, Horst K. Hahn, Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim, Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert, Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt, Kewei Yan, Yonghong Yan, Zhe Tang, Jianqiang Ma, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi, Nicholas J. Tustison, Brian B. Avants, Philip Cook, James C. Gee, Lin Tian, Hastings Greer, Marc Niethammer, Andrew Hoopes, Malte Hoffmann, Adrian V. Dalca, Stergios Christodoulidis, Theo Estiene, Maria Vakalopoulou, Nikos Paragios, Daniel S. Marcus, Christos Davatzikos, Aristeidis Sotiras, Bjoern Menze, Spyridon Bakas, Diana Waldmannstetter
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.
1 code implementation • 13 Dec 2021 • SungMin Hong, Anna K. Bonkhoff, Andrew Hoopes, Martin Bretzner, Markus D. Schirmer, Anne-Katrin Giese, Adrian V. Dalca, Polina Golland, Natalia S. Rost
However, multiple per-image annotations are often not available in a real-world application and the uncertainty does not provide full control on segmentation results to users.
no code implementations • 8 Dec 2021 • Alessa Hering, Lasse Hansen, Tony C. W. Mok, Albert C. S. Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sulaiman Vesal, Mirabela Rusu, Geoffrey Sonn, Théo Estienne, Maria Vakalopoulou, Luyi Han, Yunzhi Huang, Pew-Thian Yap, Mikael Brudfors, Yaël Balbastre, Samuel Joutard, Marc Modat, Gal Lifshitz, Dan Raviv, Jinxin Lv, Qiang Li, Vincent Jaouen, Dimitris Visvikis, Constance Fourcade, Mathieu Rubeaux, Wentao Pan, Zhe Xu, Bailiang Jian, Francesca De Benetti, Marek Wodzinski, Niklas Gunnarsson, Jens Sjölund, Daniel Grzech, Huaqi Qiu, Zeju Li, Alexander Thorley, Jinming Duan, Christoph Großbröhmer, Andrew Hoopes, Ingerid Reinertsen, Yiming Xiao, Bennett Landman, Yuankai Huo, Keelin Murphy, Nikolas Lessmann, Bram van Ginneken, Adrian V. Dalca, Mattias P. Heinrich
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed.
1 code implementation • 20 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.
2 code implementations • 20 Jul 2021 • Benjamin Billot, Douglas N. Greve, Oula Puonti, Axel Thielscher, Koen van Leemput, Bruce Fischl, Adrian V. Dalca, Juan Eugenio Iglesias
Here we introduce SynthSeg, the first segmentation CNN robust against changes in contrast and resolution.
no code implementations • 6 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.
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.
1 code implementation • 13 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.
2 code implementations • ICCV 2021 • Neel Dey, Mengwei Ren, Adrian V. Dalca, Guido Gerig
Deformable templates are essential to large-scale medical image registration, segmentation, and population analysis.
1 code implementation • 4 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.
2 code implementations • 6 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.
1 code implementation • 4 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.
1 code implementation • 9 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.
1 code implementation • 29 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.
1 code implementation • 2 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.
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.
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.
no code implementations • 21 Apr 2020 • Malte Hoffmann, Benjamin Billot, Douglas N. Greve, Juan Eugenio Iglesias, Bruce Fischl, Adrian V. Dalca
This approach results in powerful networks that accurately generalize to a broad array of MRI contrasts.
2 code implementations • 21 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.
1 code implementation • 9 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.
no code implementations • 23 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.
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.
Ranked #1 on Brain Segmentation on Brain MRI segmentation
no code implementations • 5 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.
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.
no code implementations • 25 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.
no code implementations • ICCV 2019 • Guha Balakrishnan, Adrian V. Dalca, Amy Zhao, John V. Guttag, Fredo Durand, William T. Freeman
We introduce visual deprojection: the task of recovering an image or video that has been collapsed along a dimension.
no code implementations • 13 Aug 2019 • Hyeon Woo Lee, Mert R. Sabuncu, Adrian V. Dalca
We tackle biomedical image segmentation in the scenario of only a few labeled brain MR images.
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.
1 code implementation • 26 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.
1 code implementation • 1 Jul 2019 • Florian Dubost, Marleen de Bruijne, Marco Nardin, Adrian V. Dalca, Kathleen L. Donahue, Anne-Katrin Giese, Mark R. Etherton, Ona Wu, Marius de Groot, Wiro Niessen, Meike Vernooij, Natalia S. Rost, Markus D. Schirmer
In this work, we propose to automatically assess the quality of registration to an atlas in clinical FLAIR MRI scans of the brain.
no code implementations • 23 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.
1 code implementation • 25 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.
1 code implementation • 8 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).
Ranked #2 on Diffeomorphic Medical Image Registration on OASIS+ADIBE+ADHD200+MCIC+PPMI+HABS+HarvardGSP
Constrained Diffeomorphic Image Registration Deformable Medical Image Registration +2
6 code implementations • 8 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.
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.
2 code implementations • CVPR 2019 • Amy Zhao, Guha Balakrishnan, Frédo Durand, John V. Guttag, Adrian V. Dalca
Image segmentation is an important task in many medical applications.
Ranked #1 on Brain Image Segmentation on T1-weighted MRI
1 code implementation • 7 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).
no code implementations • 17 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.
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.
7 code implementations • 14 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.
Ranked #1 on Diffeomorphic Medical Image Registration on OASIS+ADIBE+ADHD200+MCIC+PPMI+HABS+HarvardGSP (Dice metric)
Deformable Medical Image Registration Diffeomorphic Medical Image Registration +1
no code implementations • 11 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.
2 code implementations • 17 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.
2 code implementations • 11 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.
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.
3 code implementations • CVPR 2018 • Guha Balakrishnan, Amy Zhao, Mert R. Sabuncu, John Guttag, Adrian V. Dalca
We define registration as a parametric function, and optimize its parameters given a set of images from a collection of interest.
1 code implementation • 3 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.