4.6k citations · 5.8k across the 34 of their papers we have counts for
34 papers
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…
Airway Label Prediction in Video Bronchoscopy: Capturing Temporal Dependencies Utilizing Anatomical Knowledge
Ron Keuth, Mattias Heinrich, Martin Eichenlaub +1
Purpose: Navigation guidance is a key requirement for a multitude of lung interventions using video bronchoscopy. State-of-the-art solutions focus on lung biopsies using electromag…
A denoised Mean Teacher for domain adaptive point cloud registration
Alexander Bigalke, Mattias P. Heinrich
Point cloud-based medical registration promises increased computational efficiency, robustness to intensity shifts, and anonymity preservation but is limited by the inefficacy of u…
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…
Weakly Supervised Airway Orifice Segmentation in Video Bronchoscopy
Ron Keuth, Mattias Heinrich, Martin Eichenlaub +1
Video bronchoscopy is routinely conducted for biopsies of lung tissue suspected for cancer, monitoring of COPD patients and clarification of acute respiratory problems at intensive…
Learn2Trust: A video and streamlit-based educational programme for AI-based medical image analysis targeted towards medical students
Hanna Siebert, Marian Himstedt, Mattias Heinrich
In order to be able to use artificial intelligence (AI) in medicine without scepticism and to recognise and assess its growing potential, a basic understanding of this topic is nec…
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…
Automatic Generation of Synthetic Colonoscopy Videos for Domain Randomization
Abhishek Dinkar Jagtap, Mattias Heinrich, Marian Himstedt
An increasing number of colonoscopic guidance and assistance systems rely on machine learning algorithms which require a large amount of high-quality training data. In order to ens…
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,…
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…
Development and Characterization of a Chest CT Atlas
Kaiwen Xu, Riqiang Gao, Mirza S. Khan +8
A major goal of lung cancer screening is to identify individuals with particular phenotypes that are associated with high risk of cancer. Identifying relevant phenotypes is complic…
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 to map between ferns with differentiable binary embedding networks
Max Blendowski, Mattias P. Heinrich
Current deep learning methods are based on the repeated, expensive application of convolutions with parameter-intensive weight matrices. In this work, we present a novel concept th…
Segmentation of Retinal Low-Cost Optical Coherence Tomography Images using Deep Learning
Timo Kepp, Helge Sudkamp, Claus von der Burchard +6
The treatment of age-related macular degeneration (AMD) requires continuous eye exams using optical coherence tomography (OCT). The need for treatment is determined by the presence…
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…
Sequential Rib Labeling and Segmentation in Chest X-Ray using Mask R-CNN
Jöran Wessel, Mattias P. Heinrich, Jens von Berg +2
Mask R-CNN is a state-of-the-art network architecture for the detection and segmentation of object instances in the computer vision domain. In this contribution, it is used to loca…
Sparse Annotations with Random Walks for U-Net Segmentation of Biodegradable Bone Implants in Synchrotron Microtomograms
Niclas Bockelmann, Diana Krüger, D. C. Florian Wieland +9
Currently, most bone implants used in orthopedics and traumatology are non-degradable and may need to be surgically removed later on e.g. in the case of children. This removal is a…
Closing the Gap between Deep and Conventional Image Registration using Probabilistic Dense Displacement Networks
Mattias P. Heinrich
Nonlinear image registration continues to be a fundamentally important tool in medical image analysis. Diagnostic tasks, image-guided surgery and radiotherapy as well as motion ana…