Search Results for author: Greg Zaharchuk

Found 19 papers, 7 papers with code

Random Expert Sampling for Deep Learning Segmentation of Acute Ischemic Stroke on Non-contrast CT

no code implementations7 Sep 2023 Sophie Ostmeier, Brian Axelrod, Benjamin Pulli, Benjamin F. J. Verhaaren, Abdelkader Mahammedi, Yongkai Liu, Christian Federau, Greg Zaharchuk, Jeremy J. Heit

Conclusion: A model trained on random expert sampling can identify the presence and location of acute ischemic brain tissue on Non-Contrast CT similar to CT perfusion and with better consistency than experts.

Simulation of Arbitrary Level Contrast Dose in MRI Using an Iterative Global Transformer Model

no code implementations22 Jul 2023 Dayang Wang, Srivathsa Pasumarthi, Greg Zaharchuk, Ryan Chamberlain

In this work, we formulate a novel transformer (Gformer) based iterative modelling approach for the synthesis of images with arbitrary contrast enhancement that corresponds to different dose levels.

Tumor Segmentation

Non-inferiority of Deep Learning Acute Ischemic Stroke Segmentation on Non-Contrast CT Compared to Expert Neuroradiologists

1 code implementation24 Nov 2022 Sophie Ostmeier, Brian Axelrod, Benjamin F. J. Verhaaren, Soren Christensen, Abdelkader Mahammedi, Yongkai Liu, Benjamin Pulli, Li-Jia Li, Greg Zaharchuk, Jeremy J. Heit

The optimized model trained on expert A was compared to test experts B and C. We used a one-sided Wilcoxon signed-rank test to test for the non-inferiority of the model-expert compared to the inter-expert agreement.

Test

Brain MRI-to-PET Synthesis using 3D Convolutional Attention Networks

no code implementations22 Nov 2022 Ramy Hussein, David Shin, Moss Zhao, Jia Guo, Guido Davidzon, Michael Moseley, Greg Zaharchuk

Such methods may enable more widespread and accurate CBF evaluation in larger cohorts who cannot undergo PET imaging due to radiation concerns, lack of access, or logistic challenges.

SSIM

One Model to Synthesize Them All: Multi-contrast Multi-scale Transformer for Missing Data Imputation

no code implementations28 Apr 2022 Jiang Liu, Srivathsa Pasumarthi, Ben Duffy, Enhao Gong, Keshav Datta, Greg Zaharchuk

In this work, we formulate missing data imputation as a sequence-to-sequence learning problem and propose a multi-contrast multi-scale Transformer (MMT), which can take any subset of input contrasts and synthesize those that are missing.

Image Generation Imputation

Multi-task Deep Learning for Cerebrovascular Disease Classification and MRI-to-PET Translation

no code implementations12 Feb 2022 Ramy Hussein, Moss Zhao, David Shin, Jia Guo, Kevin T. Chen, Rui D. Armindo, Guido Davidzon, Michael Moseley, Greg Zaharchuk

Accurate quantification of cerebral blood flow (CBF) is essential for the diagnosis and assessment of cerebrovascular diseases such as Moyamoya, carotid stenosis, aneurysms, and stroke.

Multi-Task Learning SSIM +1

The RSNA-ASNR-MICCAI BraTS 2021 Benchmark on Brain Tumor Segmentation and Radiogenomic Classification

1 code implementation5 Jul 2021 Ujjwal Baid, Satyam Ghodasara, Suyash Mohan, Michel Bilello, Evan Calabrese, Errol Colak, Keyvan Farahani, Jayashree Kalpathy-Cramer, Felipe C. Kitamura, Sarthak Pati, Luciano M. Prevedello, Jeffrey D. Rudie, Chiharu Sako, Russell T. Shinohara, Timothy Bergquist, Rong Chai, James Eddy, Julia Elliott, Walter Reade, Thomas Schaffter, Thomas Yu, Jiaxin Zheng, Ahmed W. Moawad, Luiz Otavio Coelho, Olivia McDonnell, Elka Miller, Fanny E. Moron, Mark C. Oswood, Robert Y. Shih, Loizos Siakallis, Yulia Bronstein, James R. Mason, Anthony F. Miller, Gagandeep Choudhary, Aanchal Agarwal, Cristina H. Besada, Jamal J. Derakhshan, Mariana C. Diogo, Daniel D. Do-Dai, Luciano Farage, John L. Go, Mohiuddin Hadi, Virginia B. Hill, Michael Iv, David Joyner, Christie Lincoln, Eyal Lotan, Asako Miyakoshi, Mariana Sanchez-Montano, Jaya Nath, Xuan V. Nguyen, Manal Nicolas-Jilwan, Johanna Ortiz Jimenez, Kerem Ozturk, Bojan D. Petrovic, Chintan Shah, Lubdha M. Shah, Manas Sharma, Onur Simsek, Achint K. Singh, Salil Soman, Volodymyr Statsevych, Brent D. Weinberg, Robert J. Young, Ichiro Ikuta, Amit K. Agarwal, Sword C. Cambron, Richard Silbergleit, Alexandru Dusoi, Alida A. Postma, Laurent Letourneau-Guillon, Gloria J. Guzman Perez-Carrillo, Atin Saha, Neetu Soni, Greg Zaharchuk, Vahe M. Zohrabian, Yingming Chen, Milos M. Cekic, Akm Rahman, Juan E. Small, Varun Sethi, Christos Davatzikos, John Mongan, Christopher Hess, Soonmee Cha, Javier Villanueva-Meyer, John B. Freymann, Justin S. Kirby, Benedikt Wiestler, Priscila Crivellaro, Rivka R. Colen, Aikaterini Kotrotsou, Daniel Marcus, Mikhail Milchenko, Arash Nazeri, Hassan Fathallah-Shaykh, Roland Wiest, Andras Jakab, Marc-Andre Weber, Abhishek Mahajan, Bjoern Menze, Adam E. Flanders, Spyridon Bakas

The BraTS 2021 challenge celebrates its 10th anniversary and is jointly organized by the Radiological Society of North America (RSNA), the American Society of Neuroradiology (ASNR), and the Medical Image Computing and Computer Assisted Interventions (MICCAI) society.

Benchmarking Brain Tumor Segmentation +2

OUTCOMES: Rapid Under-sampling Optimization achieves up to 50% improvements in reconstruction accuracy for multi-contrast MRI sequences

no code implementations8 Mar 2021 Ke Wang, Enhao Gong, Yuxin Zhang, Suchadrima Banerjee, Greg Zaharchuk, John Pauly

Multi-contrast Magnetic Resonance Imaging (MRI) acquisitions from a single scan have tremendous potential to streamline exams and reduce imaging time.

Self-Supervised Longitudinal Neighbourhood Embedding

1 code implementation5 Mar 2021 Jiahong Ouyang, Qingyu Zhao, Ehsan Adeli, Edith V Sullivan, Adolf Pfefferbaum, Greg Zaharchuk, Kilian M Pohl

Longitudinal MRIs are often used to capture the gradual deterioration of brain structure and function caused by aging or neurological diseases.

Contrastive Learning Representation Learning

Representation Disentanglement for Multi-modal brain MR Analysis

1 code implementation23 Feb 2021 Jiahong Ouyang, Ehsan Adeli, Kilian M. Pohl, Qingyu Zhao, Greg Zaharchuk

To address this issue, we propose a margin loss that regularizes the similarity in relationships of the representations across subjects and modalities.

Brain Tumor Segmentation Disentanglement +1

Random Bundle: Brain Metastases Segmentation Ensembling through Annotation Randomization

no code implementations23 Feb 2020 Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel Rubin

We introduce a novel ensembling method, Random Bundle (RB), that improves performance for brain metastases segmentation.

Segmentation

Brain Metastasis Segmentation Network Trained with Robustness to Annotations with Multiple False Negatives

no code implementations MIDL 2019 Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Greg Zaharchuk, Daniel Rubin

Even with a simulated false negative rate as high as 50%, applying our loss function to randomly censored data preserves maximum sensitivity at 97% of the baseline with uncensored training data, compared to just 10% for a standard loss function.

Handling Missing MRI Input Data in Deep Learning Segmentation of Brain Metastases: A Multi-Center Study

no code implementations27 Dec 2019 Endre Grøvik, Darvin Yi, Michael Iv, Elizabeth Tong, Line Brennhaug Nilsen, Anna Latysheva, Cathrine Saxhaug, Kari Dolven Jacobsen, Åslaug Helland, Kyrre Eeg Emblem, Daniel Rubin, Greg Zaharchuk

A deep learning based segmentation model for automatic segmentation of brain metastases, named DropOut, was trained on multi-sequence MRI from 100 patients, and validated/tested on 10/55 patients.

Segmentation Test

MRI Pulse Sequence Integration for Deep-Learning Based Brain Metastasis Segmentation

no code implementations18 Dec 2019 Darvin Yi, Endre Grøvik, Michael Iv, Elizabeth Tong, Kyrre Eeg Emblem, Line Brennhaug Nilsen, Cathrine Saxhaug, Anna Latysheva, Kari Dolven Jacobsen, Åslaug Helland, Greg Zaharchuk, Daniel Rubin

We illustrate not only the generalizability of the network but also the utility of this robustness when applying the trained model to data from a different center, which does not use the same pulse sequences.

Small Data Image Classification

Deep Learning Enables Automatic Detection and Segmentation of Brain Metastases on Multi-Sequence MRI

no code implementations18 Mar 2019 Endre Grøvik, Darvin Yi, Michael Iv, Elisabeth Tong, Daniel L. Rubin, Greg Zaharchuk

For an optimal probability threshold, detection and segmentation performance was assessed on a per metastasis basis.

Quantitative Susceptibility Mapping using Deep Neural Network: QSMnet

1 code implementation15 Mar 2018 Jaeyeon Yoon, Enhao Gong, Itthi Chatnuntawech, Berkin Bilgic, Jingu Lee, Woojin Jung, Jingyu Ko, Hosan Jung, Kawin Setsompop, Greg Zaharchuk, Eung Yeop Kim, John Pauly, Jong-Ho Lee

The QSMnet maps of the test dataset were compared with those from TKD and MEDI for image quality and consistency in multiple head orientations.

Image and Video Processing

200x Low-dose PET Reconstruction using Deep Learning

no code implementations12 Dec 2017 Junshen Xu, Enhao Gong, John Pauly, Greg Zaharchuk

Experiments shows the proposed method can reconstruct low-dose PET image to a standard-dose quality with only two-hundredth dose.

Image Reconstruction

Deep Generative Adversarial Networks for Compressed Sensing Automates MRI

2 code implementations31 May 2017 Morteza Mardani, Enhao Gong, Joseph Y. Cheng, Shreyas Vasanawala, Greg Zaharchuk, Marcus Alley, Neil Thakur, Song Han, William Dally, John M. Pauly, Lei Xing

A multilayer convolutional neural network is then jointly trained based on diagnostic quality images to discriminate the projection quality.

MRI Reconstruction Test

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