Search Results for author: Mattias P. Heinrich

Found 26 papers, 14 papers with code

Self-supervised Learning of Dense Hierarchical Representations for Medical Image Segmentation

1 code implementation12 Jan 2024 Eytan Kats, Jochen G. Hirsch, Mattias P. Heinrich

This paper demonstrates a self-supervised framework for learning voxel-wise coarse-to-fine representations tailored for dense downstream tasks.

Image Segmentation Linear evaluation +4

DG-TTA: Out-of-domain medical image segmentation through Domain Generalization and Test-Time Adaptation

1 code implementation11 Dec 2023 Christian Weihsbach, Christian N. Kruse, Alexander Bigalke, Mattias P. Heinrich

In this study, we propose to combine domain generalization and test-time adaptation to create a highly effective approach for reusing pre-trained models in unseen target domains.

Domain Generalization Image Registration +6

Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding

1 code implementation8 Dec 2023 Hellena Hempe, Alexander Bigalke, Mattias P. Heinrich

In this study, we specifically explore the use of shape auto-encoders for vertebrae, taking advantage of advancements in automated multi-label segmentation and the availability of large datasets for unsupervised learning.

Decoder Segmentation

Unsupervised 3D registration through optimization-guided cyclical self-training

1 code implementation29 Jun 2023 Alexander Bigalke, Lasse Hansen, Tony C. W. Mok, Mattias P. Heinrich

State-of-the-art deep learning-based registration methods employ three different learning strategies: supervised learning, which requires costly manual annotations, unsupervised learning, which heavily relies on hand-crafted similarity metrics designed by domain experts, or learning from synthetic data, which introduces a domain shift.

Self-Supervised Learning

A denoised Mean Teacher for domain adaptive point cloud registration

1 code implementation26 Jun 2023 Alexander Bigalke, Mattias P. Heinrich

Self-training with the Mean Teacher is an established approach to this problem but is impaired by the inherent noise of the pseudo labels from the teacher.

Computational Efficiency Denoising +2

Chasing Clouds: Differentiable Volumetric Rasterisation of Point Clouds as a Highly Efficient and Accurate Loss for Large-Scale Deformable 3D Registration

1 code implementation ICCV 2023 Mattias P. Heinrich, Alexander Bigalke, Christoph Großbröhmer, Lasse Hansen

Learning-based registration for large-scale 3D point clouds has been shown to improve robustness and accuracy compared to classical methods and can be trained without supervision for locally rigid problems.

Self-Supervised Learning

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

1 code implementation22 Nov 2022 Alexander Bigalke, Lasse Hansen, Jasper Diesel, Carlotta Hennigs, Philipp Rostalski, Mattias P. Heinrich

As a remedy, we present a novel domain adaptation method, adapting a model from a labeled source to a shifted unlabeled target domain.

3D Human Pose Estimation Anatomy +1

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

1 code implementation1 Jul 2022 Alexander Bigalke, Lasse Hansen, Mattias P. Heinrich

We build on a keypoint-based registration model, combining graph convolutions for geometric feature learning with loopy belief optimization, and propose to reduce the domain shift through self-ensembling.

Domain Adaptation Image Registration +1

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

1 code implementation28 Feb 2022 Mattias P. Heinrich, Lasse Hansen

Extending the method to semantic features sets new stat-of-the-art performance on inter-subject abdominal CT registration.

Image Registration

The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients

no code implementations13 Dec 2021 Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari, Michel Bilello, Benedikt Wiestler, Julian Schwarting, Evan Calabrese, Jeffrey Rudie, Syed Abidi, Mina Mousa, Javier Villanueva-Meyer, Brandon K. K. Fields, Florian Kofler, Russell Takeshi Shinohara, Juan Eugenio Iglesias, Tony C. W. Mok, Albert C. S. Chung, Marek Wodzinski, Artur Jurgas, Niccolo Marini, Manfredo Atzori, Henning Muller, Christoph Grobroehmer, Hanna Siebert, Lasse Hansen, Mattias P. Heinrich, Luca Canalini, Jan Klein, Annika Gerken, Stefan Heldmann, Alessa Hering, Horst K. Hahn, Mingyuan Meng, Lei Bi, Dagan Feng, Jinman Kim, Ramy A. Zeineldin, Mohamed E. Karar, Franziska Mathis-Ullrich, Oliver Burgert, Javid Abderezaei, Aymeric Pionteck, Agamdeep Chopra, Mehmet Kurt, Kewei Yan, Yonghong Yan, Zhe Tang, Jianqiang Ma, Sahar Almahfouz Nasser, Nikhil Cherian Kurian, Mohit Meena, Saqib Shamsi, Amit Sethi, Nicholas J. Tustison, Brian B. Avants, Philip Cook, James C. Gee, Lin Tian, Hastings Greer, Marc Niethammer, Andrew Hoopes, Malte Hoffmann, Adrian V. Dalca, Stergios Christodoulidis, Theo Estiene, Maria Vakalopoulou, Nikos Paragios, Daniel S. Marcus, Christos Davatzikos, Aristeidis Sotiras, Bjoern Menze, Spyridon Bakas, Diana Waldmannstetter

Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance.

Descriptive Image Registration +1

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

1 code implementation6 Dec 2021 Hanna Siebert, Lasse Hansen, Mattias P. Heinrich

Current approaches for deformable medical image registration often struggle to fulfill all of the following criteria: versatile applicability, small computation or training times, and the being able to estimate large deformations.

Deformable Medical Image Registration Image Registration +1

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

1 code implementation1 Mar 2021 Lasse Hansen, Mattias P. Heinrich

As in other areas of medical image analysis, e. g. semantic segmentation, deep learning is currently driving the development of new approaches for image registration.

Decoder Descriptive +3

Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies

no code implementations28 May 2020 Mattias P. Heinrich, Lasse Hansen

We believe that unsupervised domain adaptation can be beneficial in overcoming the current limitations for multimodal registration, where good metrics are hard to define.

Image Registration Unsupervised Domain Adaptation

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

no code implementations MIDL 2019 Lasse Hansen, Mattias P. Heinrich

Though, deep learning based medical image registration is currently starting to show promising advances, often, it still fells behind conventional frameworks in terms of registration accuracy.

Image Registration Medical Image Registration

Learning to map between ferns with differentiable binary embedding networks

no code implementations MIDL 2019 Max Blendowski, Mattias P. Heinrich

Current deep learning methods are based on the repeated, expensive application of convolutions with parameter-intensive weight matrices.

Binary Classification

Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning

no code implementations23 Jan 2020 Timo Kepp, Helge Sudkamp, Claus von der Burchard, Hendrik Schenke, Peter Koch, Gereon Hüttmann, Johann Roider, Mattias P. Heinrich, Heinz Handels

It is shown that the CNN-based approach can segment the retina with high accuracy, whereas the segmentation of the PED proves to be challenging.


Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients

no code implementations17 Sep 2019 Lasse Hansen, Doris Dittmer, Mattias P. Heinrich

Our results indicate that the inherent geometric structure of the extracted keypoints is sufficient to establish descriptive point features, which yield a significantly improved performance and robustness of our registration framework.


Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks

2 code implementations25 Jul 2019 Mattias P. Heinrich

Nonlinear image registration continues to be a fundamentally important tool in medical image analysis.

Image Registration

Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds

no code implementations14 Sep 2018 Lasse Hansen, Jasper Diesel, Mattias P. Heinrich

Graph convolutional networks are a new promising learning approach to deal with data on irregular domains.

TernaryNet: Faster Deep Model Inference without GPUs for Medical 3D Segmentation using Sparse and Binary Convolutions

1 code implementation29 Jan 2018 Mattias P. Heinrich, Max Blendowski, Ozan Oktay

We propose a new scheme that approximates both trainable weights and neural activations in deep networks by ternary values and tackles the open question of backpropagation when dealing with non-differentiable functions.

Open-Ended Question Answering Pancreas Segmentation +1

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