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Tom Brosch
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Journal Articles
- 2022
- [j5]Danielle F. Pace
, Adrian V. Dalca, Tom Brosch, Tal Geva, Andrew J. Powell, Jürgen Weese
, Mehdi Hedjazi Moghari, Polina Golland:
Learned iterative segmentation of highly variable anatomy from limited data: Applications to whole heart segmentation for congenital heart disease. Medical Image Anal. 80: 102469 (2022) - 2021
- [j4]Andra-Iza Iuga
, Heike Carolus, Anna J. Höink, Tom Brosch, Tobias Klinder, David Maintz, Thorsten Persigehl, Bettina Baeßler, Michael Püsken:
Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks. BMC Medical Imaging 21(1): 69 (2021) - 2017
- [j3]Ferran Prados
, John Ashburner, Claudia Blaiotta, Tom Brosch, Julio Carballido-Gamio, Manuel Jorge Cardoso
, Benjamin N. Conrad
, Esha Datta, Gergely Dávid
, Benjamin De Leener, Sara M. Dupont, Patrick Freund
, Claudia A. M. Gandini Wheeler-Kingshott
, Francesco Grussu
, Roland G. Henry, Bennett A. Landman, Emil Ljungberg
, Bailey Lyttle, Sébastien Ourselin
, Nico Papinutto, Salvatore Saporito, Regina Schlaeger
, Seth A. Smith, Paul E. Summers
, Roger C. Tam
, Marios C. Yiannakas, Alyssa H. Zhu, Julien Cohen-Adad:
Spinal cord grey matter segmentation challenge. NeuroImage 152: 312-329 (2017) - 2016
- [j2]Tom Brosch, Lisa Y. W. Tang
, Youngjin Yoo, David K. B. Li, Anthony Traboulsee, Roger C. Tam
:
Deep 3D Convolutional Encoder Networks With Shortcuts for Multiscale Feature Integration Applied to Multiple Sclerosis Lesion Segmentation. IEEE Trans. Medical Imaging 35(5): 1229-1239 (2016) - 2015
- [j1]Tom Brosch, Roger C. Tam
:
Efficient Training of Convolutional Deep Belief Networks in the Frequency Domain for Application to High-Resolution 2D and 3D Images. Neural Comput. 27(1): 211-227 (2015)
Conference and Workshop Papers
- 2020
- [c16]Heike Carolus, Andra-Iza Iuga, Tom Brosch, Rafael Wiemker, Frank Thiele, Anna J. Höink, David Maintz, Michael Püsken, Tobias Klinder:
Automated detection and segmentation of mediastinal and axillary lymph nodes from CT using foveal fully convolutional networks. Medical Imaging: Computer-Aided Diagnosis 2020 - 2018
- [c15]Eliza Orasanu, Tom Brosch, Carri Glide-Hurst, Steffen Renisch:
Organ-At-Risk Segmentation in Brain MRI Using Model-Based Segmentation: Benefits of Deep Learning-Based Boundary Detectors. ShapeMI@MICCAI 2018: 291-299 - [c14]Danielle F. Pace, Adrian V. Dalca, Tom Brosch, Tal Geva, Andrew J. Powell, Jürgen Weese, Mehdi Hedjazi Moghari, Polina Golland:
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease. DLMIA/ML-CDS@MICCAI 2018: 334-342 - [c13]Tom Brosch, Jochen Peters, Alexandra Groth, Thomas Stehle, Jürgen Weese:
Deep Learning-Based Boundary Detection for Model-Based Segmentation with Application to MR Prostate Segmentation. MICCAI (4) 2018: 515-522 - [c12]Tom Brosch, Axel Saalbach:
Foveal fully convolutional nets for multi-organ segmentation. Medical Imaging: Image Processing 2018: 105740U - [c11]Evelin Hristova, Heinrich Schulz, Tom Brosch, Mattias P. Heinrich
, Hannes Nickisch
:
Nearest neighbor 3D segmentation with context features. Medical Imaging: Image Processing 2018: 105740M - [c10]Cristian Lorenz, Tom Brosch, Cybèle Ciofolo-Veit
, Tobias Klinder, Thierry Lefevre, Angelo Cavallaro, Ibtisam Salim, Aris T. Papageorghiou
, Caroline Raynaud, David Roundhill, Laurence Rouet
, Nicole Schadewaldt, Alexander Schmidt-Richberg:
Automated abdominal plane and circumference estimation in 3D US for fetal screening. Medical Imaging: Image Processing 2018: 105740I - [c9]Cristian Lorenz, Nora Vogt, Peter Börnert, Tom Brosch:
Spine centerline extraction and efficient spine reading of MRI and CT data. Medical Imaging: Image Processing 2018: 1057425 - 2017
- [c8]Alexander Schmidt-Richberg, Tom Brosch, Nicole Schadewaldt, Tobias Klinder, Angelo Cavallaro, Ibtisam Salim, David Roundhill, Aris T. Papageorghiou
, Cristian Lorenz:
Abdomen Segmentation in 3D Fetal Ultrasound Using CNN-powered Deformable Models. FIFI/OMIA@MICCAI 2017: 52-61 - [c7]Adam Porisky, Tom Brosch, Emil Ljungberg
, Lisa Y. W. Tang, Youngjin Yoo, Benjamin De Leener, Anthony Traboulsee, Julien Cohen-Adad, Roger C. Tam
:
Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections. DLMIA/ML-CDS@MICCAI 2017: 330-337 - 2016
- [c6]Youngjin Yoo, Lisa Y. W. Tang, Tom Brosch, David K. B. Li, Luanne Metz, Anthony Traboulsee, Roger C. Tam
:
Deep Learning of Brain Lesion Patterns for Predicting Future Disease Activity in Patients with Early Symptoms of Multiple Sclerosis. LABELS/DLMIA@MICCAI 2016: 86-94 - [c5]Lisa Y. W. Tang
, Tom Brosch, Xingtong Liu
, Youngjin Yoo, Anthony Traboulsee, David K. B. Li, Roger C. Tam
:
Corpus Callosum Segmentation in Brain MRIs via Robust Target-Localization and Joint Supervised Feature Extraction and Prediction. MICCAI (2) 2016: 406-414 - 2015
- [c4]Tom Brosch, Youngjin Yoo, Lisa Y. W. Tang, David K. B. Li, Anthony Traboulsee, Roger C. Tam
:
Deep Convolutional Encoder Networks for Multiple Sclerosis Lesion Segmentation. MICCAI (3) 2015: 3-11 - 2014
- [c3]Youngjin Yoo, Tom Brosch, Anthony Traboulsee, David K. B. Li, Roger C. Tam
:
Deep Learning of Image Features from Unlabeled Data for Multiple Sclerosis Lesion Segmentation. MLMI 2014: 117-124 - [c2]Tom Brosch, Youngjin Yoo, David K. B. Li, Anthony Traboulsee
, Roger C. Tam
:
Modeling the Variability in Brain Morphology and Lesion Distribution in Multiple Sclerosis by Deep Learning. MICCAI (2) 2014: 462-469 - 2013
- [c1]Tom Brosch, Roger C. Tam
:
Manifold Learning of Brain MRIs by Deep Learning. MICCAI (2) 2013: 633-640
Informal and Other Publications
- 2020
- [i2]Cheryl Sital, Tom Brosch, Dominique Tio, Alexander Raaijmakers, Jürgen Weese:
3D medical image segmentation with labeled and unlabeled data using autoencoders at the example of liver segmentation in CT images. CoRR abs/2003.07923 (2020) - 2018
- [i1]Danielle F. Pace, Adrian V. Dalca, Tom Brosch, Tal Geva, Andrew J. Powell, Jürgen Weese, Mehdi Hedjazi Moghari, Polina Golland:
Iterative Segmentation from Limited Training Data: Applications to Congenital Heart Disease. CoRR abs/1809.04182 (2018)
Coauthor Index
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