no code implementations • 29 Mar 2024 • Molin Zhang, Polina Golland, Patricia Ellen Grant, Elfar Adalsteinsson
In this study, we introduce FetalDiffusion, a novel approach utilizing a conditional diffusion model to generate 3D synthetic fetal MRI with controllable pose.
no code implementations • 4 Feb 2024 • Peiqi Wang, Yikang Shen, Zhen Guo, Matthew Stallone, Yoon Kim, Polina Golland, Rameswar Panda
Our experiments demonstrate that the proposed diversity measure in the normalized weight gradient space is correlated with downstream instruction-following performance.
1 code implementation • 21 Dec 2023 • Benjamin Billot, Neel Dey, Daniel Moyer, Malte Hoffmann, Esra Abaci Turk, Borjan Gagoski, Ellen Grant, Polina Golland
Here we propose EquiTrack, the first method that uses recent steerable SE(3)-equivariant CNNs (E-CNN) for motion tracking.
1 code implementation • 11 Dec 2023 • Vivek Gopalakrishnan, Neel Dey, Polina Golland
Preoperatively, a CNN is trained to regress the pose of a randomly oriented synthetic X-ray rendered from the preoperative CT.
1 code implementation • 8 Dec 2023 • S. Mazdak Abulnaga, Neel Dey, Sean I. Young, Eileen Pan, Katherine I. Hobgood, Clinton J. Wang, P. Ellen Grant, Esra Abaci Turk, Polina Golland
In this work, we propose a machine learning segmentation framework for placental BOLD MRI and apply it to segmenting each volume in a time series.
1 code implementation • 5 Dec 2023 • Sean I. Young, Yaël Balbastre, Bruce Fischl, Polina Golland, Juan Eugenio Iglesias
Here, we propose a SVR method that overcomes the shortcomings of previous work and produces state-of-the-art reconstructions in the presence of extreme inter-slice motion.
1 code implementation • 6 Nov 2023 • Zeen Chi, Zhongxiao Cong, Clinton J. Wang, Yingcheng Liu, Esra Abaci Turk, P. Ellen Grant, S. Mazdak Abulnaga, Polina Golland, Neel Dey
We apply our method to learning subject-specific atlases and motion stabilization of dynamic BOLD MRI time-series of fetuses in utero.
no code implementations • 30 Oct 2023 • Keegan Quigley, Miriam Cha, Josh Barua, Geeticka Chauhan, Seth Berkowitz, Steven Horng, Polina Golland
Vision-language pretraining has been shown to produce high-quality visual encoders which transfer efficiently to downstream computer vision tasks.
no code implementations • 8 Oct 2023 • Dominik Hollidt, Clinton Wang, Polina Golland, Marc Pollefeys
We present a novel approach to perform 3D semantic segmentation solely from 2D supervision by leveraging Neural Radiance Fields (NeRFs).
1 code implementation • 5 Oct 2023 • Yingcheng Liu, Neerav Karani, Neel Dey, S. Mazdak Abulnaga, Junshen Xu, P. Ellen Grant, Esra Abaci Turk, Polina Golland
The placenta plays a crucial role in fetal development.
1 code implementation • 24 Jul 2023 • Clinton J. Wang, Polina Golland
One little-explored frontier of image generation and editing is the task of interpolating between two input images, a feature missing from all currently deployed image generation pipelines.
no code implementations • 16 Jul 2023 • Neerav Karani, Neel Dey, Polina Golland
Neural network prediction probabilities and accuracy are often only weakly-correlated.
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 • 5 May 2023 • Henry F. J. Tregidgo, Sonja Soskic, Mark D. Olchanyi, Juri Althonayan, Benjamin Billot, Chiara Maffei, Polina Golland, Anastasia Yendiki, Daniel C. Alexander, Martina Bocchetta, Jonathan D. Rohrer, Juan Eugenio Iglesias
Some tools have attempted to incorporate information from diffusion MRI in the segmentation to refine these boundaries, but do not generalise well across diffusion MRI acquisitions.
no code implementations • 25 Apr 2023 • Peiqi Wang, Yingcheng Liu, Ching-Yun Ko, William M. Wells, Seth Berkowitz, Steven Horng, Polina Golland
Self-supervised representation learning on image-text data facilitates crucial medical applications, such as image classification, visual grounding, and cross-modal retrieval.
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 • 11 Dec 2022 • Peiqi Wang, William M. Wells, Seth Berkowitz, Steven Horng, Polina Golland
Image-text multimodal representation learning aligns data across modalities and enables important medical applications, e. g., image classification, visual grounding, and cross-modal retrieval.
no code implementations • 28 Sep 2022 • Deborah Pereg, Martin Villiger, Brett Bouma, Polina Golland
The statistical supervised learning framework assumes an input-output set with a joint probability distribution that is reliably represented by the training dataset.
2 code implementations • 26 Aug 2022 • Vivek Gopalakrishnan, Polina Golland
To make DRRs interoperable with gradient-based optimization and deep learning frameworks, we have reformulated Siddon's method, the most popular ray-tracing algorithm used in DRR generation, as a series of vectorized tensor operations.
no code implementations • 5 Aug 2022 • Keegan Quigley, Miriam Cha, Ruizhi Liao, Geeticka Chauhan, Steven Horng, Seth Berkowitz, Polina Golland
In this paper, we build a data-efficient learning framework that utilizes radiology reports to improve medical image classification performance with limited labeled data (fewer than 1000 examples).
1 code implementation • 4 Aug 2022 • S. Mazdak Abulnaga, Sean I. Young, Katherine Hobgood, Eileen Pan, Clinton J. Wang, P. Ellen Grant, Esra Abaci Turk, Polina Golland
In this work, we propose a machine learning model based on a U-Net neural network architecture to automatically segment the placenta in BOLD MRI and apply it to segmenting each volume in a time series.
1 code implementation • 22 Jun 2022 • Junshen Xu, Daniel Moyer, P. Ellen Grant, Polina Golland, Juan Eugenio Iglesias, Elfar Adalsteinsson
Experiments with real-world MRI data are also performed to demonstrate the ability of the proposed model to improve the quality of 3D reconstruction under severe fetal motion.
1 code implementation • 2 Jun 2022 • Clinton J. Wang, Polina Golland
With the emergence of powerful representations of continuous data in the form of neural fields, there is a need for discretization invariant learning: an approach for learning maps between functions on continuous domains without being sensitive to how the function is sampled.
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 • 5 Feb 2022 • S. Mazdak Abulnaga, Oded Stein, Polina Golland, Justin Solomon
Although shape correspondence is a central problem in geometry processing, most methods for this task apply only to two-dimensional surfaces.
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.
1 code implementation • 15 Nov 2021 • S. Mazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev, P. Ellen Grant, Justin Solomon, Polina Golland
However, due to the curved and highly variable in vivo shape of the placenta, interpreting and visualizing these images is difficult.
no code implementations • 13 Nov 2021 • Peiqi Wang, Ruizhi Liao, Daniel Moyer, Seth Berkowitz, Steven Horng, Polina Golland
We define consistent evidence to be both compatible and sufficient with respect to model predictions.
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.
1 code implementation • 23 Jun 2021 • Junshen Xu, Esra Abaci Turk, P. Ellen Grant, Polina Golland, Elfar Adalsteinsson
Fetal motion is unpredictable and rapid on the scale of conventional MR scan times.
1 code implementation • 11 May 2021 • Mohammad Haft-Javaherian, Martin Villiger, Kenichiro Otsuka, Joost Daemen, Peter Libby, Polina Golland, Brett E. Bouma
Intravascular ultrasound and optical coherence tomography are widely available for characterizing coronary stenoses and provide critical vessel parameters to optimize percutaneous intervention.
no code implementations • 18 Mar 2021 • Daniel Moyer, Esra Abaci Turk, P Ellen Grant, William M. Wells, Polina Golland
The transformation is then derived in closed form from the outputs of the filters.
1 code implementation • 8 Mar 2021 • Ruizhi Liao, Daniel Moyer, Miriam Cha, Keegan Quigley, Seth Berkowitz, Steven Horng, Polina Golland, William M. Wells
We propose and demonstrate a representation learning approach by maximizing the mutual information between local features of images and text.
no code implementations • 15 Jan 2021 • Daniel Moyer, Polina Golland
We show that for a wide class of harmonization/domain-invariance schemes several undesirable properties are unavoidable.
1 code implementation • 24 Dec 2020 • Juan Eugenio Iglesias, Benjamin Billot, Yael Balbastre, Azadeh Tabari, John Conklin, Daniel C. Alexander, Polina Golland, Brian L. Edlow, Bruce Fischl
Most existing algorithms for automatic 3D morphometry of human brain MRI scans are designed for data with near-isotropic voxels at approximately 1 mm resolution, and frequently have contrast constraints as well - typically requiring T1 scans (e. g., MP-RAGE).
2 code implementations • 8 Dec 2020 • Razvan V Marinescu, Daniel Moyer, Polina Golland
Our method, Bayesian Reconstruction through Generative Models (BRGM), uses a single pre-trained generator model to solve different image restoration tasks, i. e., super-resolution and in-painting, by combining it with different forward corruption models.
Ranked #1 on Image Denoising on FFHQ 64x64 - 4x upscaling
no code implementations • NeurIPS 2020 • Alireza Mehrtash, Purang Abolmaesumi, Polina Golland, Tina Kapur, Demian Wassermann, William M. Wells III
In most experiments, PEP provides a small improvement in performance, and, in some cases, a substantial improvement in empirical calibration.
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 • 5 Oct 2020 • Ruizhi Liao, Daniel Moyer, Polina Golland, William M. Wells
Estimating mutual information between continuous random variables is often intractable and extremely challenging for high-dimensional data.
1 code implementation • 22 Aug 2020 • Geeticka Chauhan, Ruizhi Liao, William Wells, Jacob Andreas, Xin Wang, Seth Berkowitz, Steven Horng, Peter Szolovits, Polina Golland
To take advantage of the rich information present in the radiology reports, we develop a neural network model that is trained on both images and free-text to assess pulmonary edema severity from chest radiographs at inference time.
1 code implementation • 13 Aug 2020 • Steven Horng, Ruizhi Liao, Xin Wang, Sandeep Dalal, Polina Golland, Seth J. Berkowitz
Results: The area under the receiver operating characteristic curve (AUC) for differentiating alveolar edema from no edema was 0. 99 for the semi-supervised model and 0. 87 for the pre-trained models.
no code implementations • 16 Jul 2020 • Molin Zhang, Junshen Xu, Esra Abaci Turk, P. Ellen Grant, Polina Golland, Elfar Adalsteinsson
The proposed DRL for fetal pose landmark search demonstrates a potential clinical utility for online detection of fetal motion that guides real-time mitigation of motion artifacts as well as health diagnosis during MRI of the pregnant mother.
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 • 23 Jun 2020 • Junshen Xu, Sayeri Lala, Borjan Gagoski, Esra Abaci Turk, P. Ellen Grant, Polina Golland, Elfar Adalsteinsson
The proposed method is also implemented and evaluated on an MR scanner, which demonstrates the feasibility of online image quality assessment and image reacquisition during fetal MR scans.
no code implementations • 27 Apr 2020 • Polina Golland, Jack Gallant, Greg Hager, Hanspeter Pfister, Christos Papadimitriou, Stefan Schaal, Joshua T. Vogelstein
In December 2014, a two-day workshop supported by the Computing Community Consortium (CCC) and the National Science Foundation's Computer and Information Science and Engineering Directorate (NSF CISE) was convened in Washington, DC, with the goal of bringing together computer scientists and brain researchers to explore these new opportunities and connections, and develop a new, modern dialogue between the two research communities.
4 code implementations • 9 Feb 2020 • Razvan V. Marinescu, Neil P. Oxtoby, Alexandra L. Young, Esther E. Bron, Arthur W. Toga, Michael W. Weiner, Frederik Barkhof, Nick C. Fox, Arman Eshaghi, Tina Toni, Marcin Salaterski, Veronika Lunina, Manon Ansart, Stanley Durrleman, Pascal Lu, Samuel Iddi, Dan Li, Wesley K. Thompson, Michael C. Donohue, Aviv Nahon, Yarden Levy, Dan Halbersberg, Mariya Cohen, Huiling Liao, Tengfei Li, Kaixian Yu, Hongtu Zhu, Jose G. Tamez-Pena, Aya Ismail, Timothy Wood, Hector Corrada Bravo, Minh Nguyen, Nanbo Sun, Jiashi Feng, B. T. Thomas Yeo, Gang Chen, Ke Qi, Shiyang Chen, Deqiang Qiu, Ionut Buciuman, Alex Kelner, Raluca Pop, Denisa Rimocea, Mostafa M. Ghazi, Mads Nielsen, Sebastien Ourselin, Lauge Sorensen, Vikram Venkatraghavan, Keli Liu, Christina Rabe, Paul Manser, Steven M. Hill, James Howlett, Zhiyue Huang, Steven Kiddle, Sach Mukherjee, Anais Rouanet, Bernd Taschler, Brian D. M. Tom, Simon R. White, Noel Faux, Suman Sedai, Javier de Velasco Oriol, Edgar E. V. Clemente, Karol Estrada, Leon Aksman, Andre Altmann, Cynthia M. Stonnington, Yalin Wang, Jianfeng Wu, Vivek Devadas, Clementine Fourrier, Lars Lau Raket, Aristeidis Sotiras, Guray Erus, Jimit Doshi, Christos Davatzikos, Jacob Vogel, Andrew Doyle, Angela Tam, Alex Diaz-Papkovich, Emmanuel Jammeh, Igor Koval, Paul Moore, Terry J. Lyons, John Gallacher, Jussi Tohka, Robert Ciszek, Bruno Jedynak, Kruti Pandya, Murat Bilgel, William Engels, Joseph Cole, Polina Golland, Stefan Klein, Daniel C. Alexander
TADPOLE's unique results suggest that current prediction algorithms provide sufficient accuracy to exploit biomarkers related to clinical diagnosis and ventricle volume, for cohort refinement in clinical trials for Alzheimer's disease.
no code implementations • 17 Jul 2019 • Bernhard Egger, Markus D. Schirmer, Florian Dubost, Marco J. Nardin, Natalia S. Rost, Polina Golland
We propose and demonstrate a joint model of anatomical shapes, image features and clinical indicators for statistical shape modeling and medical image analysis.
no code implementations • 10 Jul 2019 • Junshen Xu, Molin Zhang, Esra Abaci Turk, Larry Zhang, Ellen Grant, Kui Ying, Polina Golland, Elfar Adalsteinsson
The performance and diagnostic utility of magnetic resonance imaging (MRI) in pregnancy is fundamentally constrained by fetal motion.
2 code implementations • 21 May 2019 • Razvan V. Marinescu, Arman Eshaghi, Daniel C. Alexander, Polina Golland
Compared to existing visualisation software (i. e. Freesurfer, SPM, 3D Slicer), BrainPainter has three key advantages: (1) it does not require the input data to be in a specialised format, allowing BrainPainter to be used in combination with any neuroimaging analysis tools, (2) it can visualise both cortical and subcortical structures and (3) it can be used to generate movies showing dynamic processes, e. g. propagation of pathology on the brain.
Graphics Image and Video Processing
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 • 12 Mar 2019 • S. Mazdak Abulnaga, Esra Abaci Turk, Mikhail Bessmeltsev, P. Ellen Grant, Justin Solomon, Polina Golland
We formulate our method as a map from the in vivo shape to a flattened template that minimizes the symmetric Dirichlet energy to control distortion throughout the volume.
no code implementations • 11 Mar 2019 • Juan Eugenio Iglesias, Koen van Leemput, Polina Golland, Anastasia Yendiki
Segmentation of structural and diffusion MRI (sMRI/dMRI) is usually performed independently in neuroimaging pipelines.
no code implementations • 6 Mar 2019 • Ruizhi Liao, Esra A. Turk, Miaomiao Zhang, Jie Luo, Elfar Adalsteinsson, P. Ellen Grant, Polina Golland
To achieve accurate and robust alignment, we make a Markov assumption on the nature of motion and take advantage of the temporal smoothness in the image data.
no code implementations • 27 Feb 2019 • Ruizhi Liao, Jonathan Rubin, Grace Lam, Seth Berkowitz, Sandeep Dalal, William Wells, Steven Horng, Polina Golland
We propose and demonstrate machine learning algorithms to assess the severity of pulmonary edema in chest x-ray images of congestive heart failure patients.
2 code implementations • 11 Jan 2019 • Razvan V. Marinescu, Marco Lorenzi, Stefano B. Blumberg, Alexandra L. Young, Pere P. Morell, Neil P. Oxtoby, Arman Eshaghi, Keir X. Yong, Sebastian J. Crutch, Polina Golland, Daniel C. Alexander
DKT infers robust multimodal biomarker trajectories in rare neurodegenerative diseases even when only limited, unimodal data is available, by transferring information from larger multimodal datasets from common neurodegenerative diseases.
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.
no code implementations • 22 Jun 2018 • Christian Wachinger, Matthew Toews, Georg Langs, William Wells, Polina Golland
We introduce an approach for image segmentation based on sparse correspondences between keypoints in testing and training images.
no code implementations • 20 Mar 2018 • Jie Luo, Matt Toews, Ines Machado, Sarah Frisken, Miaomiao Zhang, Frank Preiswerk, Alireza Sedghi, Hongyi Ding, Steve Pieper, Polina Golland, Alexandra Golby, Masashi Sugiyama, William M. Wells III
Kernels of the GP are estimated by using variograms and a discrete grid search method.
no code implementations • 12 Aug 2016 • Ruizhi Liao, Esra Turk, Miaomiao Zhang, Jie Luo, Ellen Grant, Elfar Adalsteinsson, Polina Golland
We present a robust method to correct for motion and deformations for in-utero volumetric MRI time series.
no code implementations • 6 Oct 2015 • George Chen, Devavrat Shah, Polina Golland
Despite the popularity and empirical success of patch-based nearest-neighbor and weighted majority voting approaches to medical image segmentation, there has been no theoretical development on when, why, and how well these nonparametric methods work.
no code implementations • 11 Mar 2015 • Christian Wachinger, Polina Golland
High computational costs of manifold learning prohibit its application for large point sets.
no code implementations • 23 Nov 2014 • Nematollah Kayhan Batmanghelich, Gerald Quon, Alex Kulesza, Manolis Kellis, Polina Golland, Luke Bornn
We propose a novel diverse feature selection method based on determinantal point processes (DPPs).
no code implementations • 22 Mar 2013 • George H. Chen, Christian Wachinger, Polina Golland
To this end, out-of-sample extensions are applied by constructing an interpolation function that maps from the input space to the low-dimensional manifold.
no code implementations • NeurIPS 2010 • Georg Langs, Yanmei Tie, Laura Rigolo, Alexandra Golby, Polina Golland
This advantage is pronounced for subjects with tumors that affect the language areas and thus cause spatial reorganization of the functional regions.
no code implementations • NeurIPS 2010 • Danial Lashkari, Ramesh Sridharan, Polina Golland
We present a model that describes the structure in the responses of different brain areas to a set of stimuli in terms of stimulus categories" (clusters of stimuli) and "functional units" (clusters of voxels).