Publications by authors named "Tom Brosch"

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Automated segmentation of 3D cine cardiovascular magnetic resonance imaging.
Soroosh Tayebi Arasteh, Jennifer Romanowicz, Danielle F Pace, Polina Golland, Andrew J Powell, Tom Brosch

Front Cardiovasc Med· October 2023

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Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease.
Danielle F Pace, Adrian V Dalca, Tom Brosch, Tal Geva, Andrew J Powell

Med Image Anal· August 2022

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Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks.
Andra-Iza Iuga, Heike Carolus, Anna J Höink, Tom Brosch, Tobias Klinder

BMC Med Imaging· April 2021

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Organ-At-Risk Segmentation in Brain MRI using Model-Based Segmentation: Benefits of Deep Learning-Based Boundary Detectors.
Eliza Orasanu, Tom Brosch, Carri Glide-Hurst, Steffen Renisch

Shape Med Imaging (2018)· September 2018

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Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease.
Danielle F Pace, Adrian V Dalca, Tom Brosch, Tal Geva, Andrew J Powell

Deep Learn Med Image Anal Multimodal Learn Clin Decis Support (2018)· September 2018

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Deep learning of joint myelin and T1w MRI features in normal-appearing brain tissue to distinguish between multiple sclerosis patients and healthy controls.
Youngjin Yoo, Lisa Y W Tang, Tom Brosch, David K B Li, Shannon Kolind

Neuroimage Clin· June 2018

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Spinal cord grey matter segmentation challenge.
Ferran Prados, John Ashburner, Claudia Blaiotta, Tom Brosch, Julio Carballido-Gamio

Neuroimage· May 2017

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Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation.
Tom Brosch, Lisa Y W Tang, Youngjin Yoo, David K B Li, Anthony Traboulsee

IEEE Trans Med Imaging· May 2016

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Efficient training of convolutional deep belief networks in the frequency domain for application to high-resolution 2D and 3D images.
Tom Brosch, Roger Tam

Neural Comput· January 2015

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Modeling the variability in brain morphology and lesion distribution in multiple sclerosis by deep learning.
Tom Brosch, Youngjin Yoo, David K B Li, Anthony Traboulsee, Roger Tam

Med Image Comput Comput Assist Interv· January 2015

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Manifold learning of brain MRIs by deep learning.
Tom Brosch, Roger Tam,

Med Image Comput Comput Assist Interv· April 2014

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