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ORCIDMattias P. Heinrich
Mattias Paul Heinrich
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- affiliation: University of Lübeck, Institute of Medical Informatics (IMI), Germany
- affiliation: University of Oxford, Institute of Biomedical Engineering, UK
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2020 – today
- 2023
[j39]Niklas Hermes
, Alexander Bigalke, Mattias P. Heinrich:
Point cloud-based scene flow estimation on realistically deformable objects: A benchmark of deep learning-based methods. J. Vis. Commun. Image Represent. 95: 103893 (2023)- [j38]Reuben Dorent, Aaron Kujawa, Marina Ivory, Spyridon Bakas, Nicola Rieke, Samuel Joutard, Ben Glocker, M. Jorge Cardoso, Marc Modat, Kayhan Batmanghelich, Arseniy Belkov, Maria G. Baldeon Calisto, Jae Won Choi, Benoit M. Dawant, Hexin Dong, Sergio Escalera, Yubo Fan, Lasse Hansen, Mattias P. Heinrich, Smriti Joshi, Victoriya Kashtanova, Hyeongyu Kim, Satoshi Kondo, Christian N. Kruse, Susana K. Lai-Yuen, Hao Li, Han Liu, Buntheng Ly, Ipek Oguz, Hyungseob Shin, Boris Shirokikh, Zixian Su, Guotai Wang, Jianghao Wu, Yanwu Xu, Kai Yao, Li Zhang, Sébastien Ourselin, Jonathan Shapey, Tom Vercauteren:
CrossMoDA 2021 challenge: Benchmark of cross-modality domain adaptation techniques for vestibular schwannoma and cochlea segmentation. Medical Image Anal. 83: 102628 (2023) - [j37]Alexander Bigalke, Lasse Hansen, Jasper Diesel, Carlotta Hennigs, Philipp Rostalski, Mattias P. Heinrich:
Anatomy-guided domain adaptation for 3D in-bed human pose estimation. Medical Image Anal. 89: 102887 (2023) - [j36]Mattias P. Heinrich, Hanna Siebert, Laura Graf, Sven Mischkewitz, Lasse Hansen:
Robust and Realtime Large Deformation Ultrasound Registration Using End-to-End Differentiable Displacement Optimisation. Sensors 23(6): 2876 (2023) - [j35]Kumar T. Rajamani, Priya Rani, Hanna Siebert, Elagiri Ramalingam Rajkumar, Mattias P. Heinrich:
Attention-augmented U-Net (AA-U-Net) for semantic segmentation. Signal Image Video Process. 17(4): 981-989 (2023) - [j34]Alessa Hering, Lasse Hansen, Tony C. W. Mok, Albert C. S. Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sulaiman Vesal, Mirabela Rusu, Geoffrey A. Sonn, Théo Estienne, Maria Vakalopoulou, Luyi Han, Yunzhi Huang, Pew-Thian Yap, Mikael Brudfors, Yaël Balbastre, Samuel Joutard, Marc Modat, Gal Lifshitz, Dan Raviv, Jinxin Lv, Qiang Li, Vincent Jaouen, Dimitris Visvikis, Constance Fourcade, Mathieu Rubeaux, Wentao Pan, Zhe Xu, Bailiang Jian, Francesca De Benetti, Marek Wodzinski, Niklas Gunnarsson, Jens Sjölund, Daniel Grzech, Huaqi Qiu, Zeju Li, Alexander Thorley, Jinming Duan, Christoph Großbröhmer, Andrew Hoopes, Ingerid Reinertsen, Yiming Xiao, Bennett A. Landman, Yuankai Huo, Keelin Murphy, Nikolas Lessmann, Bram van Ginneken, Adrian V. Dalca, Mattias P. Heinrich:
Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning. IEEE Trans. Medical Imaging 42(3): 697-712 (2023) - [c87]Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, A. Emre Kavur, Oldrich Kodym, Michal Kozubek, Jianning Li, Hongwei Bran Li, Jun Ma, Carlos Martín-Isla, Bjoern H. Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, Jonathan Rafael-Patino, Vivek Singh Bawa, Stefanie Speidel, Carole H. Sudre, Kimberlin M. H. van Wijnen, M. Wagner, D. Wei, Amine Yamlahi, Moi Hoon Yap, C. Yuan, Maximilian Zenk, A. Zia, David Zimmerer, Dogu Baran Aydogan, Binod Bhattarai, Louise Bloch, Raphael Brüngel, J. Cho, C. Choi, Q. Dou, Ivan Ezhov, Christoph M. Friedrich, C. Fuller, Rebati Raman Gaire, Adrian Galdran, Álvaro García-Faura, Maria Grammatikopoulou, S. Hong, Mostafa Jahanifar, I. Jang, Abdolrahim Kadkhodamohammadi, I. Kang, Florian Kofler, S. Kondo, Hugo Jaco Kuijf, M. Li, M. Luu, Tomaz Martincic, P. Morais, M. A. Naser, B. Oliveira, D. Owen, S. Pang, J. Park, S. Park, S. Plotka, Élodie Puybareau, Nasir M. Rajpoot, K. Ryu, N. Saeed, Adam Shephard, P. Shi, Dejan Stepec, Ronast Subedi, Guillaume Tochon, Helena R. Torres, Hélène Urien, João L. Vilaça, Kareem A. Wahid, H. Wang, J. Wang, L. Wang, X. Wang, Benedikt Wiestler, Marek Wodzinski, F. Xia, J. Xie, Z. Xiong, S. Yang, Y. Yang, Z. Zhao, Klaus H. Maier-Hein, Paul F. Jäger, Annette Kopp-Schneider, Lena Maier-Hein:
Why is the Winner the Best? CVPR 2023: 19955-19966 - [c86]Alexander Bigalke, Mattias P. Heinrich:
A Denoised Mean Teacher for Domain Adaptive Point Cloud Registration. MICCAI (10) 2023: 666-676 - [c85]Alexander Bigalke, Lasse Hansen, Tony C. W. Mok, Mattias P. Heinrich:
Unsupervised 3D Registration Through Optimization-Guided Cyclical Self-training. MICCAI (10) 2023: 677-687 - [c84]Christoph Großbröhmer, Mattias P. Heinrich:
Generalised 3D Medical Image Registration with Learned Shape Encodings. MIUA 2023: 268-280 - [i31]Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Sharib Ali, Vincent Andrearczyk, Marc Aubreville, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Veronika Cheplygina, Marie Daum, Marleen de Bruijne, Adrien Depeursinge, Reuben Dorent, Jan Egger, David G. Ellis, Sandy Engelhardt, Melanie Ganz, Noha M. Ghatwary, Gabriel Girard, Patrick Godau, Anubha Gupta, Lasse Hansen, Kanako Harada, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Pierre Jannin, Ali Emre Kavur, Oldrich Kodym, Michal Kozubek, Jianning Li, Hongwei Bran Li, Jun Ma, Carlos Martín-Isla, Bjoern H. Menze, J. Alison Noble, Valentin Oreiller, Nicolas Padoy, Sarthak Pati, Kelly Payette, Tim Rädsch, et al.:
Why is the winner the best? CoRR abs/2303.17719 (2023) - [i30]Alexander Bigalke, Mattias P. Heinrich:
A denoised Mean Teacher for domain adaptive point cloud registration. CoRR abs/2306.14749 (2023) - [i29]Alexander Bigalke, Lasse Hansen, Tony C. W. Mok, Mattias P. Heinrich:
Unsupervised 3D registration through optimization-guided cyclical self-training. CoRR abs/2306.16997 (2023) - [i28]Ron Keuth, Mattias P. Heinrich, Martin Eichenlaub, Marian Himstedt:
Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge. CoRR abs/2307.08318 (2023) - 2022
- [j33]Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes B. Fogo, Raymond Harris, Mark P. de Caestecker, Mattias P. Heinrich, Jeffrey M. Spraggins, Yuankai Huo, Bennett A. Landman:
Multi-contrast computed tomography healthy kidney atlas. Comput. Biol. Medicine 146: 105555 (2022) - [j32]Pullalarevu Karthik, Mansi Parashar, S. Sofana Reka, Kumar T. Rajamani, Mattias P. Heinrich:
Semantic segmentation for plant phenotyping using advanced deep learning pipelines. Multim. Tools Appl. 81(3): 4535-4547 (2022) - [j31]Hanna Siebert, Lasse Hansen, Mattias P. Heinrich:
Learning a Metric for Multimodal Medical Image Registration without Supervision Based on Cycle Constraints. Sensors 22(3): 1107 (2022) - [c83]Mona Schumacher, Ragnar Bade, Andreas Genz, Mattias P. Heinrich:
Iterative 3D CNN Based Segmentation of Vascular Trees in Liver CT. Bildverarbeitung für die Medizin 2022: 7-12 - [c82]Hellena Hempe, Mattias P. Heinrich:
Abstract: Light-weight Semantic Segmentation and Labelling of Vertebrae in 3D-CT Scans. Bildverarbeitung für die Medizin 2022: 19 - [c81]Fenja Falta, Lasse Hansen, Marian Himstedt, Mattias P. Heinrich:
Learning an Airway Atlas from Lung CT Using Semantic Inter-patient Deformable Registration. Bildverarbeitung für die Medizin 2022: 75-80 - [c80]Niklas Hermes, Lasse Hansen, Alexander Bigalke, Mattias P. Heinrich:
Support Point Sets for Improving Contactless Interaction in Geometric Learning for Hand Pose Estimation. Bildverarbeitung für die Medizin 2022: 89-94 - [c79]Laura Graf, Sven Mischkewitz, Lasse Hansen, Mattias P. Heinrich:
Spatiotemporal Attention for Realtime Segmentation of Corrupted Sequential Ultrasound Data - Improving Usability of AI-based Image Guidance. Bildverarbeitung für die Medizin 2022: 235-240 - [c78]Christoph Großbröhmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich:
Employing ConvexAdam for BraTS-Reg. BrainLes@MICCAI 2022: 252-261 - [c77]Christian Weihsbach, Lasse Hansen, Mattias P. Heinrich:
XEdgeConv: Leveraging graph convolutions for efficient, permutation- and rotation-invariant dense 3D medical image segmentation. GeoMedIA 2022: 61-71 - [c76]Alexander Bigalke, Lasse Hansen, Mattias P. Heinrich:
Adapting the Mean Teacher for Keypoint-Based Lung Registration Under Geometric Domain Shifts. MICCAI (6) 2022: 280-290 - [c75]Fenja Falta, Lasse Hansen, Mattias P. Heinrich:
Learning Iterative Optimisation for Deformable Image Registration of Lung CT with Recurrent Convolutional Networks. MICCAI (6) 2022: 301-309 - [c74]Alexander Bigalke, Lasse Hansen, Jasper Diesel, Mattias P. Heinrich:
Domain adaptation through anatomical constraints for 3d human pose estimation under the cover. MIDL 2022: 173-187 - [c73]Christian Weihsbach, Alexander Bigalke, Christian N. Kruse, Hellena Hempe, Mattias P. Heinrich:
DeepSTAPLE: Learning to Predict Multimodal Registration Quality for Unsupervised Domain Adaptation. WBIR 2022: 37-46 - [c72]Mattias P. Heinrich, Lasse Hansen:
Voxelmorph++ - Going Beyond the Cranial Vault with Keypoint Supervision and Multi-channel Instance Optimisation. WBIR 2022: 85-95 - [c71]Hanna Siebert, Mattias P. Heinrich:
Learn to Fuse Input Features for Large-Deformation Registration with Differentiable Convex-Discrete Optimisation. WBIR 2022: 119-123 - [c70]Till Nicke, Laura Graf, Mikko Lauri, Sven Mischkewitz, Simone Frintrop, Mattias P. Heinrich:
Realtime Optical Flow Estimation on Vein and Artery Ultrasound Sequences Based on Knowledge-Distillation. WBIR 2022: 134-143 - [c69]Mona Schumacher, Hanna Siebert, Ragnar Bade, Andreas Genz, Mattias P. Heinrich:
Weak Bounding Box Supervision for Image Registration Networks. WBIR 2022: 215-219 - [e4]Marc Aubreville, David Zimmerer, Mattias P. Heinrich:
Biomedical Image Registration, Domain Generalisation and Out-of-Distribution Analysis - MICCAI 2021 Challenges: MIDOG 2021, MOOD 2021, and Learn2Reg 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27 - October 1, 2021, Proceedings. Lecture Notes in Computer Science 13166, Springer 2022, ISBN 978-3-030-97280-6 [contents] - [e3]Alessa Hering, Julia A. Schnabel, Miaomiao Zhang, Enzo Ferrante, Mattias P. Heinrich, Daniel Rueckert:
Biomedical Image Registration - 10th International Workshop, WBIR 2022, Munich, Germany, July 10-12, 2022, Proceedings. Lecture Notes in Computer Science 13386, Springer 2022, ISBN 978-3-031-11202-7 [contents] - [i27]Reuben Dorent, Aaron Kujawa, Marina Ivory, Spyridon Bakas, Nicola Rieke, Samuel Joutard, Ben Glocker, Jorge Cardoso, Marc Modat, Kayhan Batmanghelich, Arseniy Belkov, Maria G. Baldeon Calisto, Jae Won Choi, Benoit M. Dawant, Hexin Dong, Sergio Escalera, Yubo Fan, Lasse Hansen, Mattias P. Heinrich, Smriti Joshi, Victoriya Kashtanova, Hyeongyu Kim, Satoshi Kondo, Christian N. Kruse, Susana K. Lai-Yuen, Hao Li, Han Liu, Buntheng Ly, Ipek Oguz, Hyungseob Shin, Boris Shirokikh, Zixian Su, Guotai Wang, Jianghao Wu, Yanwu Xu, Kai Yao, Li Zhang, Sébastien Ourselin, Jonathan Shapey, Tom Vercauteren:
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation. CoRR abs/2201.02831 (2022) - [i26]Mattias P. Heinrich, Lasse Hansen:
Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation. CoRR abs/2203.00046 (2022) - [i25]Abhishek Dinkar Jagtap, Mattias P. Heinrich, Marian Himstedt:
Automatic Generation of Synthetic Colonoscopy Videos for Domain Randomization. CoRR abs/2205.10368 (2022) - [i24]Alexander Bigalke, Lasse Hansen, Mattias P. Heinrich:
Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts. CoRR abs/2207.00371 (2022) - [i23]Hanna Siebert, Marian Himstedt, Mattias P. Heinrich:
Learn2Trust: A video and streamlit-based educational programme for AI-based medical image analysis targeted towards medical students. CoRR abs/2208.07314 (2022) - [i22]Ron Keuth, Mattias P. Heinrich, Martin Eichenlaub, Marian Himstedt:
Weakly Supervised Airway Orifice Segmentation in Video Bronchoscopy. CoRR abs/2208.11468 (2022) - [i21]Alexander Bigalke, Lasse Hansen, Jasper Diesel, Carlotta Hennigs, Philipp Rostalski, Mattias P. Heinrich:
Anatomy-guided domain adaptation for 3D in-bed human pose estimation. CoRR abs/2211.12193 (2022) - [i20]Matthias Eisenmann, Annika Reinke, Vivienn Weru, Minu Dietlinde Tizabi, Fabian Isensee, Tim J. Adler, Patrick Godau, Veronika Cheplygina, Michal Kozubek, Sharib Ali, Anubha Gupta, Jan Kybic, J. Alison Noble, Carlos Ortiz-de-Solórzano, Samiksha Pachade, Caroline Petitjean, Daniel Sage, Donglai Wei, Elizabeth Wilden, Deepak Alapatt, Vincent Andrearczyk, Ujjwal Baid, Spyridon Bakas, Niranjan Balu, Sophia Bano, Vivek Singh Bawa, Jorge Bernal, Sebastian Bodenstedt, Alessandro Casella, Jinwook Choi, Olivier Commowick, Marie Daum, Adrien Depeursinge, Reuben Dorent, Jan Egger, Hannah Eichhorn, Sandy Engelhardt, Melanie Ganz, Gabriel Girard, Lasse Hansen, Mattias P. Heinrich, Nicholas Heller, Alessa Hering, Arnaud Huaulmé, Hyunjeong Kim, Bennett A. Landman, Hongwei Bran Li, Jianning Li, Jun Ma, Anne L. Martel, et al.:
Biomedical image analysis competitions: The state of current participation practice. CoRR abs/2212.08568 (2022) - 2021
- [j30]Alexander Bigalke, Lasse Hansen, Jasper Diesel, Mattias P. Heinrich:
Seeing under the cover with a 3D U-Net: point cloud-based weight estimation of covered patients. Int. J. Comput. Assist. Radiol. Surg. 16(12): 2079-2087 (2021) - [j29]In Young Ha, Mattias P. Heinrich:
Modality-agnostic self-supervised deep feature learning and fast instance optimisation for multimodal fusion in ultrasound-guided interventions. Comput. Methods Programs Biomed. 211: 106374 (2021) - [j28]Kumar T. Rajamani, Hanna Siebert, Mattias P. Heinrich:
Dynamic deformable attention network (DDANet) for COVID-19 lesions semantic segmentation. J. Biomed. Informatics 119: 103816 (2021) - [j27]Max Blendowski, Lasse Hansen, Mattias P. Heinrich:
Weakly-supervised learning of multi-modal features for regularised iterative descent in 3D image registration. Medical Image Anal. 67: 101822 (2021) - [j26]Bernhard Kainz, Mattias P. Heinrich, Antonios Makropoulos, Jonas Oppenheimer, Ramin Mandegaran, Shrinivasan Sankar, Christopher Deane, Sven Mischkewitz, Fouad Al-Noor, Andrew C. Rawdin, Andreas Ruttloff, Matthew D. Stevenson, Peter Klein-Weigel, Nicola S. Curry:
Non-invasive diagnosis of deep vein thrombosis from ultrasound imaging with machine learning. npj Digit. Medicine 4 (2021) - [j25]Lasse Hansen, Mattias P. Heinrich:
GraphRegNet: Deep Graph Regularisation Networks on Sparse Keypoints for Dense Registration of 3D Lung CTs. IEEE Trans. Medical Imaging 40(9): 2246-2257 (2021) - [c68]Alexander Bigalke, Mattias P. Heinrich:
Fusing Posture and Position Representations for Point Cloud-Based Hand Gesture Recognition. 3DV 2021: 617-626 - [c67]Mona Schumacher, Daniela Frey, In Young Ha, Ragnar Bade, Andreas Genz, Mattias P. Heinrich:
Semantically Guided 3D Abdominal Image Registration with Deep Pyramid Feature Learning. Bildverarbeitung für die Medizin 2021: 16-21 - [c66]Hanna Siebert, Lasse Hansen, Mattias P. Heinrich:
Evaluating Design Choices for Deep Learning Registration Networks - Architecture Matters. Bildverarbeitung für die Medizin 2021: 111-116 - [c65]Lasse Hansen, Mattias P. Heinrich:
Abstract: Probabilistic Dense Displacement Networks for Medical Image Registration - Contributions to the Learn2Reg Challenge. Bildverarbeitung für die Medizin 2021: 125-126 - [c64]Christian N. Kruse, Lasse Hansen, Mattias P. Heinrich:
Multi-modal Unsupervised Domain Adaptation for Deformable Registration Based on Maximum Classifier Discrepancy. Bildverarbeitung für die Medizin 2021: 192-197 - [c63]Alexander Bigalke, Lasse Hansen, Mattias P. Heinrich:
End-to-end Learning of Body Weight Prediction from Point Clouds with Basis Point Sets. Bildverarbeitung für die Medizin 2021: 254-259 - [c62]Lasse Hansen, Mattias P. Heinrich:
Deep Learning Based Geometric Registration for Medical Images: How Accurate Can We Get Without Visual Features? IPMI 2021: 18-30 - [c61]Hanna Siebert, Lasse Hansen, Mattias P. Heinrich:
Fast 3D Registration with Accurate Optimisation and Little Learning for Learn2Reg 2021. MIDOG/MOOD/Learn2Reg@MICCAI 2021: 174-179 - [c60]Lasse Hansen, Mattias P. Heinrich:
Revisiting Iterative Highly Efficient Optimisation Schemes in Medical Image Registration. MICCAI (4) 2021: 203-212 - [e2]Nadya Shusharina, Mattias P. Heinrich, Ruobing Huang:
Segmentation, Classification, and Registration of Multi-modality Medical Imaging Data - MICCAI 2020 Challenges, ABCs 2020, L2R 2020, TN-SCUI 2020, Held in Conjunction with MICCAI 2020, Lima, Peru, October 4-8, 2020, Proceedings. Lecture Notes in Computer Science 12587, Springer 2021, ISBN 978-3-030-71826-8 [contents] - [e1]Mattias P. Heinrich, Qi Dou, Marleen de Bruijne, Jan Lellmann, Alexander Schlaefer, Floris Ernst:
Medical Imaging with Deep Learning, 7-9 July 2021, Lübeck, Germany. Proceedings of Machine Learning Research 143, PMLR 2021 [contents] - [i19]Lasse Hansen, Mattias P. Heinrich:
Deep learning based geometric registration for medical images: How accurate can we get without visual features? CoRR abs/2103.00885 (2021) - [i18]Hanna Siebert, Lasse Hansen, Mattias P. Heinrich:
Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021. CoRR abs/2112.03053 (2021) - [i17]Alessa Hering, Lasse Hansen, Tony C. W. Mok, Albert C. S. Chung, Hanna Siebert, Stephanie Häger, Annkristin Lange, Sven Kuckertz, Stefan Heldmann, Wei Shao, Sulaiman Vesal, Mirabela Rusu, Geoffrey A. Sonn, Théo Estienne, Maria Vakalopoulou, Luyi Han, Yunzhi Huang, Mikael Brudfors, Yaël Balbastre, Samuel Joutard, Marc Modat, Gal Lifshitz, Dan Raviv, Jinxin Lv, Qiang Li, Vincent Jaouen, Dimitris Visvikis, Constance Fourcade, Mathieu Rubeaux, Wentao Pan, Zhe Xu, Bailiang Jian, Francesca De Benetti, Marek Wodzinski, Niklas Gunnarsson, Huaqi Qiu, Zeju Li, Christoph Großbröhmer, Andrew Hoopes, Ingerid Reinertsen, Yiming Xiao, Bennett A. Landman, Yuankai Huo, Keelin Murphy, Bram van Ginneken, Adrian V. Dalca, Mattias P. Heinrich:
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning. CoRR abs/2112.04489 (2021) - 2020
- [j24]Max Blendowski, Nassim Bouteldja, Mattias P. Heinrich:
Multimodal 3D medical image registration guided by shape encoder-decoder networks. Int. J. Comput. Assist. Radiol. Surg. 15(2): 269-276 (2020) - [j23]In Young Ha, Matthias Wilms, Mattias P. Heinrich:
Semantically Guided Large Deformation Estimation with Deep Networks. Sensors 20(5): 1392 (2020) - [j22]Yiming Xiao, Andreas Maier, Wolfgang Wein, Roozbeh Shams, Samuel Kadoury, David Drobny, Marc Modat, Ingerid Reinertsen, Hassan Rivaz, Matthieu Chabanas, Maryse Fortin, Inês Machado, Yangming Ou, Mattias P. Heinrich, Julia A. Schnabel, Xia Zhong:
Evaluation of MRI to Ultrasound Registration Methods for Brain Shift Correction: The CuRIOUS2018 Challenge. IEEE Trans. Medical Imaging 39(3): 777-786 (2020) - [c59]Lasse Hansen, Maximilian Blendowski, Mattias P. Heinrich:
Abstract: Defence of Mathematical Models for Deep Learning based Registration. Bildverarbeitung für die Medizin 2020: 32 - [c58]Christian Lucas, Linda F. Aulmann, André Kemmling, Amir Madany Mamlouk, Mattias P. Heinrich:
Abstract: Estimation of the Principal Ischaemic Stroke Growth Directions for Predicting Tissue Outcomes. Bildverarbeitung für die Medizin 2020: 143 - [c57]Timo Kepp, Helge Sudkamp, Claus von der Burchard, Hendrik Schenke, Peter Koch, Gereon Hüttmann, Johann Roider, Mattias P. Heinrich, Heinz Handels:
Abstract: Segmentation of Retinal Low-Cost Optical Coherence Tomography Images Using Deep Learning. Bildverarbeitung für die Medizin 2020: 183 - [c56]Maximilian Blendowski, Mattias P. Heinrich:
Abstract: Self-Supervised 3D Context Feature Learning on Unlabeled Volume Data. Bildverarbeitung für die Medizin 2020: 192 - [c55]Hanna Siebert, Mattias P. Heinrich:
Deep Groupwise Registration of MRI Using Deforming Autoencoders. Bildverarbeitung für die Medizin 2020: 236-241 - [c54]Ron Keuth, Lasse Hansen, Mattias P. Heinrich:
Der Einfluss von Segmentierung auf die Genauigkeit eines CNN-Klassifikators zur Mimik-Steuerung. Bildverarbeitung für die Medizin 2020: 294-300 - [c53]Lasse Hansen, Mattias P. Heinrich:
Discrete Unsupervised 3D Registration Methods for the Learn2Reg Challenge. MICCAI (Challenges) 2020: 68-73 - [c52]Mattias P. Heinrich, Lasse Hansen:
Highly Accurate and Memory Efficient Unsupervised Learning-Based Discrete CT Registration Using 2.5D Displacement Search. MICCAI (3) 2020: 190-200 - [c51]Mona Schumacher, Andreas Genz, Mattias P. Heinrich:
Weakly supervised pancreas segmentation based on class activation maps. Medical Imaging: Image Processing 2020: 1131314 - [i16]Timo Kepp, Helge Sudkamp, Claus von der Burchard, Hendrik Schenke, Peter Koch, Gereon Hüttmann, Johann Roider, Mattias P. Heinrich, Heinz Handels:
Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning. CoRR abs/2001.08480 (2020) - [i15]Max Blendowski, Mattias P. Heinrich:
Learning to map between ferns with differentiable binary embedding networks. CoRR abs/2005.12563 (2020) - [i14]Lasse Hansen, Mattias P. Heinrich:
Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings. CoRR abs/2005.13338 (2020) - [i13]Mattias P. Heinrich, Lasse Hansen:
Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies. CoRR abs/2005.14107 (2020) - [i12]Kaiwen Xu, Riqiang Gao, Mirza S. Khan, Shunxing Bao, Yucheng Tang, Steve A. Deppen, Yuankai Huo, Kim L. Sandler, Pierre P. Massion, Mattias P. Heinrich, Bennett A. Landman:
Development and Characterization of a Chest CT Atlas. CoRR abs/2012.03124 (2020) - [i11]Ho Hin Lee, Yucheng Tang, Kaiwen Xu, Shunxing Bao, Agnes B. Fogo, Raymond Harris, Mark P. de Caestecker, Mattias P. Heinrich, Jeffrey M. Spraggins, Yuankai Huo, Bennett A. Landman:
Multi-Contrast Computed Tomography Healthy Kidney Atlas. CoRR abs/2012.12432 (2020)
2010 – 2019
- 2019
- [j21]Max Blendowski, Mattias P. Heinrich:
Combining MRF-based deformable registration and deep binary 3D-CNN descriptors for large lung motion estimation in COPD patients. Int. J. Comput. Assist. Radiol. Surg. 14(1): 43-52 (2019) - [j20]Lasse Hansen, Marlin Siebert, Jasper Diesel, Mattias P. Heinrich:
Fusing information from multiple 2D depth cameras for 3D human pose estimation in the operating room. Int. J. Comput. Assist. Radiol. Surg. 14(11): 1871-1879 (2019) - [j19]Alessa Hering, Sven Kuckertz, Stefan Heldmann, Mattias P. Heinrich:
Memory-efficient 2.5D convolutional transformer networks for multi-modal deformable registration with weak label supervision applied to whole-heart CT and MRI scans. Int. J. Comput. Assist. Radiol. Surg. 14(11): 1901-1912 (2019) - [j18]Jo Schlemper, Ozan Oktay, Michiel Schaap, Mattias P. Heinrich, Bernhard Kainz, Ben Glocker, Daniel Rueckert:
Attention gated networks: Learning to leverage salient regions in medical images. Medical Image Anal. 53: 197-207 (2019) - [j17]Mattias P. Heinrich, Ozan Oktay, Nassim Bouteldja:
OBELISK-Net: Fewer layers to solve 3D multi-organ segmentation with sparse deformable convolutions. Medical Image Anal. 54: 1-9 (2019) - [j16]Xiahai Zhuang, Lei Li, Christian Payer, Darko Stern, Martin Urschler, Mattias P. Heinrich, Julien Oster, Chunliang Wang, Örjan Smedby, Cheng Bian, Xin Yang, Pheng-Ann Heng, Aliasghar Mortazi, Ulas Bagci, Guanyu Yang, Chenchen Sun, Gaetan Galisot, Jean-Yves Ramel, Guang Yang:
Evaluation of algorithms for Multi-Modality Whole Heart Segmentation: An open-access grand challenge. Medical Image Anal. 58 (2019) - [j15]In Young Ha, Matthias Wilms, Heinz Handels, Mattias P. Heinrich:
Model-Based Sparse-to-Dense Image Registration for Realtime Respiratory Motion Estimation in Image-Guided Interventions. IEEE Trans. Biomed. Eng. 66(2): 302-310 (2019) - [c50]Nassim Bouteldja, Dorit Merhof, Jan Ehrhardt, Mattias P. Heinrich:
Deep Multi-Modal Encoder-Decoder Networks for Shape Constrained Segmentation and Joint Representation Learning. Bildverarbeitung für die Medizin 2019: 23-28 - [c49]Christian Lucas, Jonas J. Schöttler, André Kemmling, Linda F. Aulmann, Mattias P. Heinrich:
Automatic Detection and Segmentation of the Acute Vessel Thrombus in Cerebral CT. Bildverarbeitung für die Medizin 2019: 74-79 - [c48]Lasse Hansen, Jasper Diesel, Mattias P. Heinrich:
Regularized Landmark Detection with CAEs for Human Pose Estimation in the Operating Room. Bildverarbeitung für die Medizin 2019: 178-183 - [c47]Jannis Hagenah, Mattias P. Heinrich, Floris Ernst:
Abstract: Deep Transfer Learning for Aortic Root Dilation Identification in 3D Ultrasound Images. Bildverarbeitung für die Medizin 2019: 198 - [c46]Alessa Hering, Sven Kuckertz, Stefan Heldmann, Mattias P. Heinrich:
Enhancing Label-Driven Deep Deformable Image Registration with Local Distance Metrics for State-of-the-Art Cardiac Motion Tracking. Bildverarbeitung für die Medizin 2019: 309-314 - [c45]Christian Lucas, Linda F. Aulmann, André Kemmling, Amir Madany Mamlouk, Mattias P. Heinrich:
Estimation of the Principal Ischaemic Stroke Growth Directions for Predicting Tissue Outcomes. BrainLes@MICCAI (1) 2019: 69-79 - [c44]Timo Kepp, Jan Ehrhardt, Mattias P. Heinrich, Gereon Hüttmann, Heinz Handels:
Topology-Preserving Shape-Based Regression Of Retinal Layers In Oct Image Data Using Convolutional Neural Networks. ISBI 2019: 1437-1440 - [c43]Mattias P. Heinrich:
Closing the Gap Between Deep and Conventional Image Registration Using Probabilistic Dense Displacement Networks. MICCAI (6) 2019: 50-58 - [c42]Lasse Hansen, Doris Dittmer, Mattias P. Heinrich:
Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients. GLMI@MICCAI 2019: 53-61 - [c41]In Young Ha, Mattias P. Heinrich:
Comparing Deep Learning Strategies and Attention Mechanisms of Discrete Registration for Multimodal Image-Guided Interventions. LABELS/HAL-MICCAI/CuRIOUS@MICCAI 2019: 145-151 - [c40]Maximilian Blendowski, Hannes Nickisch, Mattias P. Heinrich:
How to Learn from Unlabeled Volume Data: Self-supervised 3D Context Feature Learning. MICCAI (6) 2019: 649-657 - [c39]Max Blendowski, Mattias P. Heinrich:
Learning interpretable multi-modal features for alignment with supervised iterative descent. MIDL