Search Results for author: Polina Golland

Found 45 papers, 19 papers with code

SVoRT: Iterative Transformer for Slice-to-Volume Registration in Fetal Brain MRI

1 code implementation22 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.

3D Reconstruction

Deep Learning on Implicit Neural Datasets

no code implementations2 Jun 2022 Clinton J. Wang, Polina Golland

Implicit neural representations (INRs) have become fast, lightweight tools for storing continuous data, but to date there is no general method for learning directly with INRs as a data representation.

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

Symmetric Volume Maps

no code implementations5 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.

Volumetric Parameterization of the Placenta to a Flattened Template

1 code implementation15 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.

Local Distortion

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

Harmonization and the Worst Scanner Syndrome

no code implementations15 Jan 2021 Daniel Moyer, Polina Golland

We show that for a wide class of harmonization/domain-invariance schemes several undesirable properties are unavoidable.

Joint super-resolution and synthesis of 1 mm isotropic MP-RAGE volumes from clinical MRI exams with scans of different orientation, resolution and contrast

1 code implementation24 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).

Image Registration Skull Stripping +1

Bayesian Image Reconstruction using Deep Generative Models

2 code implementations8 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.

Image Denoising Image Inpainting +4

PEP: Parameter Ensembling by Perturbation

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.

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.

DEMI: Discriminative Estimator of Mutual Information

1 code implementation5 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.

Representation Learning

Joint Modeling of Chest Radiographs and Radiology Reports for Pulmonary Edema Assessment

1 code implementation22 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.

Image Classification Representation Learning

Deep Learning to Quantify Pulmonary Edema in Chest Radiographs

1 code implementation13 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.

Enhanced detection of fetal pose in 3D MRI by Deep Reinforcement Learning with physical structure priors on anatomy

no code implementations16 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.

Decision Making

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

Semi-Supervised Learning for Fetal Brain MRI Quality Assessment with ROI consistency

no code implementations23 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.

Image Quality Assessment

A New Age of Computing and the Brain

no code implementations27 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.

The Alzheimer's Disease Prediction Of Longitudinal Evolution (TADPOLE) Challenge: Results after 1 Year Follow-up

4 code implementations9 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.

alzheimer's disease detection Disease Prediction

Patient-specific Conditional Joint Models of Shape, Image Features and Clinical Indicators

no code implementations17 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.

Gaussian Processes

Fetal Pose Estimation in Volumetric MRI using a 3D Convolution Neural Network

no code implementations10 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.

Pose Estimation Time Series

BrainPainter: A software for the visualisation of brain structures, biomarkers and associated pathological processes

2 code implementations21 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

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

Placental Flattening via Volumetric Parameterization

1 code implementation12 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.

Temporal Registration in Application to In-utero MRI Time Series

no code implementations6 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.

Time Series Time Series Alignment

Semi-supervised Learning for Quantification of Pulmonary Edema in Chest X-Ray Images

no code implementations27 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.

Disease Knowledge Transfer across Neurodegenerative Diseases

2 code implementations11 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.

Transfer Learning

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

Keypoint Transfer for Fast Whole-Body Segmentation

no code implementations22 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.

Semantic Segmentation

Temporal Registration in In-Utero Volumetric MRI Time Series

no code implementations12 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.

Time Series

A Latent Source Model for Patch-Based Image Segmentation

no code implementations6 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.

Medical Image Segmentation Semantic Segmentation

Sparse Projections of Medical Images onto Manifolds

no code implementations22 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.

Functional Geometry Alignment and Localization of Brain Areas

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.

Categories and Functional Units: An Infinite Hierarchical Model for Brain Activations

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).

Object Recognition Variational Inference

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