Search Results for author: Polina Golland

Found 66 papers, 32 papers with code

FetalDiffusion: Pose-Controllable 3D Fetal MRI Synthesis with Conditional Diffusion Model

no code implementations29 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.

Pose Estimation

Diversity Measurement and Subset Selection for Instruction Tuning Datasets

no code implementations4 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.

Instruction Following Point Processes

SE(3)-Equivariant and Noise-Invariant 3D Rigid Motion Tracking in Brain MRI

1 code implementation21 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.

Time Series

Intraoperative 2D/3D Image Registration via Differentiable X-ray Rendering

1 code implementation11 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.

Image Registration

Shape-aware Segmentation of the Placenta in BOLD Fetal MRI Time Series

1 code implementation8 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.

Placenta Segmentation Segmentation +1

Fully Convolutional Slice-to-Volume Reconstruction for Single-Stack MRI

no code implementations5 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.

3D Reconstruction Depth Estimation +1

Dynamic Neural Fields for Learning Atlases of 4D Fetal MRI Time-series

1 code implementation6 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.

Time Series

Bidirectional Captioning for Clinically Accurate and Interpretable Models

no code implementations30 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.

Contrastive Learning Image Captioning

Geometry Aware Field-to-field Transformations for 3D Semantic Segmentation

no code implementations8 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).

3D Semantic Segmentation Segmentation

Interpolating between Images with Diffusion Models

1 code implementation24 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.

Denoising Image Generation

Domain-agnostic segmentation of thalamic nuclei from joint structural and diffusion MRI

no code implementations5 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.


Sample-Specific Debiasing for Better Image-Text Models

no code implementations25 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.

Contrastive Learning Cross-Modal Retrieval +4

Data Consistent Deep Rigid MRI Motion Correction

1 code implementation25 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.

Image Reconstruction

Using Multiple Instance Learning to Build Multimodal Representations

no code implementations11 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.

Contrastive Learning Cross-Modal Retrieval +5

Less is More: Rethinking Few-Shot Learning and Recurrent Neural Nets

no code implementations28 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.

Computational Efficiency Deblurring +2

Fast Auto-Differentiable Digitally Reconstructed Radiographs for Solving Inverse Problems in Intraoperative Imaging

2 code implementations26 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.

3D Reconstruction

RadTex: Learning Efficient Radiograph Representations from Text Reports

no code implementations5 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).

Domain Adaptation Image Captioning +2

Automatic Segmentation of the Placenta in BOLD MRI Time Series

1 code implementation4 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.

Placenta Segmentation Time Series +1

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

Discretization Invariant Networks for Learning Maps between Neural Fields

1 code implementation2 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.

Numerical Integration

Supervision by Denoising for Medical Image Segmentation

no code implementations7 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.

Denoising Image Reconstruction +3

Symmetric Volume Maps: Order-Invariant Volumetric Mesh Correspondence with Free Boundary

1 code implementation5 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.

Anatomy 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.

Generative Adversarial Network Image Enhancement +1

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.

Anatomy regression

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.

Anatomy 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 +1

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 +5

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.

BIG-bench Machine Learning

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.

Anatomy Image Imputation +2

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.

Image Segmentation Segmentation +1

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.

Image Segmentation Medical Image Segmentation +2

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.


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

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.

Cannot find the paper you are looking for? You can Submit a new open access paper.