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Tom Brosch
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2020 – today
- 2022
- [j5]Danielle F. Pace
, Adrian V. Dalca, Tom Brosch, Tal Geva, Andrew J. Powell, Jürgen Weese
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
Automated detection and segmentation of thoracic lymph nodes from CT using 3D foveal fully convolutional neural networks. BMC Medical Imaging 21(1): 69 (2021) - 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 - [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)
2010 – 2019
- 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
Nearest neighbor 3D segmentation with context features. Medical Imaging: Image Processing 2018: 105740M - [c10]Cristian Lorenz, Tom Brosch, Cybèle Ciofolo-Veit
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 - [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) - 2017
- [j3]Ferran Prados
Spinal cord grey matter segmentation challenge. NeuroImage 152: 312-329 (2017) - [c8]Alexander Schmidt-Richberg, Tom Brosch, Nicole Schadewaldt, Tobias Klinder, Angelo Cavallaro, Ibtisam Salim, David Roundhill, Aris T. Papageorghiou
Abdomen Segmentation in 3D Fetal Ultrasound Using CNN-powered Deformable Models. FIFI/OMIA@MICCAI 2017: 52-61 - [c7]Adam Porisky, Tom Brosch, Emil Ljungberg
Grey Matter Segmentation in Spinal Cord MRIs via 3D Convolutional Encoder Networks with Shortcut Connections. DLMIA/ML-CDS@MICCAI 2017: 330-337 - 2016
- [j2]Tom Brosch, Lisa Y. W. Tang
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) - [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
Corpus Callosum Segmentation in Brain MRIs via Robust Target-Localization and Joint Supervised Feature Extraction and Prediction. MICCAI (2) 2016: 406-414 - 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) - [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
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
Coauthor Index
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