99 citations · 170 across the 16 of their papers we have counts for
17 papers
Learn2Reg 2024: New Benchmark Datasets Driving Progress on New Challenges
Lasse Hansen, Wiebke Heyer, Christoph Großbröhmer +51
Medical image registration is critical for clinical applications, and fair benchmarking of different methods is essential for monitoring ongoing progress in the field. To date, the…
Beyond the LUMIR challenge: The pathway to foundational registration models
Junyu Chen, Shuwen Wei, Joel Honkamaa +33
Medical image challenges have played a transformative role in advancing the field, catalyzing innovation and establishing new performance benchmarks. Image registration, a foundati…
Unsupervised 3D registration through optimization-guided cyclical self-training
Alexander Bigalke, Lasse Hansen, Tony C. W. Mok +1
State-of-the-art deep learning-based registration methods employ three different learning strategies: supervised learning, which requires costly manual annotations, unsupervised le…
Why is the winner the best?
Matthias Eisenmann, Annika Reinke, Vivienn Weru +122
International benchmarking competitions have become fundamental for the comparative performance assessment of image analysis methods. However, little attention has been given to in…
Biomedical image analysis competitions: The state of current participation practice
Matthias Eisenmann, Annika Reinke, Vivienn Weru +352
The number of international benchmarking competitions is steadily increasing in various fields of machine learning (ML) research and practice. So far, however, little is known abou…
Anatomy-guided domain adaptation for 3D in-bed human pose estimation
Alexander Bigalke, Lasse Hansen, Jasper Diesel +3
3D human pose estimation is a key component of clinical monitoring systems. The clinical applicability of deep pose estimation models, however, is limited by their poor generalizat…
Adapting the Mean Teacher for keypoint-based lung registration under geometric domain shifts
Alexander Bigalke, Lasse Hansen, Mattias P. Heinrich
Recent deep learning-based methods for medical image registration achieve results that are competitive with conventional optimization algorithms at reduced run times. However, deep…
Voxelmorph++ Going beyond the cranial vault with keypoint supervision and multi-channel instance optimisation
Mattias P. Heinrich, Lasse Hansen
The majority of current research in deep learning based image registration addresses inter-patient brain registration with moderate deformation magnitudes. The recent Learn2Reg med…
CrossMoDA 2021 challenge: Benchmark of Cross-Modality Domain Adaptation techniques for Vestibular Schwannoma and Cochlea Segmentation
Reuben Dorent, Aaron Kujawa, Marina Ivory +37
Domain Adaptation (DA) has recently raised strong interests in the medical imaging community. While a large variety of DA techniques has been proposed for image segmentation, most…
Fast 3D registration with accurate optimisation and little learning for Learn2Reg 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,…
The Brain Tumor Sequence Registration (BraTS-Reg) Challenge: Establishing Correspondence Between Pre-Operative and Follow-up MRI Scans of Diffuse Glioma Patients
Bhakti Baheti, Satrajit Chakrabarty, Hamed Akbari +70
Registration of longitudinal brain MRI scans containing pathologies is challenging due to dramatic changes in tissue appearance. Although there has been progress in developing gene…
Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning
Alessa Hering, Lasse Hansen, Tony C. W. Mok +50
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medi…
Deep learning based geometric registration for medical images: How accurate can we get without visual features?
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. Multi-scale en…
Unsupervised learning of multimodal image registration using domain adaptation with projected Earth Move's discrepancies
Mattias P Heinrich, Lasse Hansen
Multimodal image registration is a very challenging problem for deep learning approaches. Most current work focuses on either supervised learning that requires labelled training sc…
Tackling the Problem of Large Deformations in Deep Learning Based Medical Image Registration Using Displacement Embeddings
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 registrati…
Learning Deformable Point Set Registration with Regularized Dynamic Graph CNNs for Large Lung Motion in COPD Patients
Lasse Hansen, Doris Dittmer, Mattias P. Heinrich
Deformable registration continues to be one of the key challenges in medical image analysis. While iconic registration methods have started to benefit from the recent advances in m…
Multi-Kernel Diffusion CNNs for Graph-Based Learning on Point Clouds
Lasse Hansen, Jasper Diesel, Mattias P. Heinrich
Graph convolutional networks are a new promising learning approach to deal with data on irregular domains. They are predestined to overcome certain limitations of conventional grid…