Picture for Mattias P. Heinrich

Mattias P. Heinrich

Anatomy-guided domain adaptation for 3D in-bed human pose estimation

Nov 22, 2022
Alexander Bigalke, Lasse Hansen, Jasper Diesel, Carlotta Hennigs, Philipp Rostalski, Mattias P. Heinrich

Figure 1 for Anatomy-guided domain adaptation for 3D in-bed human pose estimation

Figure 2 for Anatomy-guided domain adaptation for 3D in-bed human pose estimation

Figure 3 for Anatomy-guided domain adaptation for 3D in-bed human pose estimation

Figure 4 for Anatomy-guided domain adaptation for 3D in-bed human pose estimation

* submitted to Medical Image Analysis

Access Paper or Ask Questions

Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts

Jul 01, 2022
Alexander Bigalke, Lasse Hansen, Mattias P. Heinrich

Figure 1 for Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts

Figure 2 for Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts

Figure 3 for Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts

* 11 pages, accepted at MICCAI 2022

Access Paper or Ask Questions

Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation

Feb 28, 2022
Mattias P. Heinrich, Lasse Hansen

Figure 1 for Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation

Figure 2 for Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation

Figure 3 for Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation

* 10 pages, accepted at WBIR 2022

Access Paper or Ask Questions

CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation

Jan 08, 2022
Reuben Dorent, Aaron Kujawa, Marina Ivory, Spyridon Bakas, Nicola Rieke, Samuel Joutard, Ben Glocker, Jorge Cardoso, Marc Modat, Kayhan Batmanghelich, Arseniy Belkov, Maria Baldeon Calisto, Jae Won Choi, Benoit M. Dawant, Hexin Dong, Sergio Escalera, Yubo Fan, Lasse Hansen, Mattias P. Heinrich, Smriti Joshi, Victoriya Kashtanova, Hyeon Gyu 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, Sebastien Ourselin, Jonathan Shapey, Tom Vercauteren

Figure 1 for CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation

Figure 2 for CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation

Figure 3 for CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation

Figure 4 for CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwnannoma and Cochlea Segmentation

* Submitted to Medical Image Analysis

Access Paper or Ask Questions

Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Dec 23, 2021
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 Sonn, Théo Estienne, Maria Vakalopoulou, Luyi Han, Yunzhi Huang, Mikael Brudfors, Yaël Balbastre, SamuelJ outard, 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, Huaqi Qiu, Zeju Li, Christoph Großbröhmer, Andrew Hoopes, Ingerid Reinertsen, Yiming Xiao, Bennett Landman, Yuankai Huo, Keelin Murphy, Nikolas Lessmann, Bram van Ginneken, Adrian V. Dalca, Mattias P. Heinrich

Figure 1 for Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Figure 2 for Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Figure 3 for Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Figure 4 for Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

Access Paper or Ask Questions

Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021

Dec 06, 2021
Hanna Siebert, Lasse Hansen, Mattias P. Heinrich

Figure 1 for Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021

Figure 2 for Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021

Access Paper or Ask Questions

Deep learning based geometric registration for medical images: How accurate can we get without visual features?

Mar 01, 2021
Lasse Hansen, Mattias P. Heinrich

Figure 1 for Deep learning based geometric registration for medical images: How accurate can we get without visual features?

Figure 2 for Deep learning based geometric registration for medical images: How accurate can we get without visual features?

Figure 3 for Deep learning based geometric registration for medical images: How accurate can we get without visual features?

Figure 4 for Deep learning based geometric registration for medical images: How accurate can we get without visual features?

* accepted at IPMI 2021

Access Paper or Ask Questions

Development and Characterization of a Chest CT Atlas

Dec 05, 2020
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

Figure 1 for Development and Characterization of a Chest CT Atlas

Figure 2 for Development and Characterization of a Chest CT Atlas

Figure 3 for Development and Characterization of a Chest CT Atlas

Figure 4 for Development and Characterization of a Chest CT Atlas

* Accepted by SPIE2021 Medical Imaging (oral)

Access Paper or Ask Questions

Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings

May 27, 2020
Lasse Hansen, Mattias P. Heinrich

Figure 1 for Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings

Figure 2 for Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings

Access Paper or Ask Questions