15 citations · 17 across the 6 of their papers we have counts for
6 papers
Shape Matters: Detecting Vertebral Fractures Using Differentiable Point-Based Shape Decoding
Hellena Hempe, Alexander Bigalke, Mattias P. Heinrich
Degenerative spinal pathologies are highly prevalent among the elderly population. Timely diagnosis of osteoporotic fractures and other degenerative deformities facilitates proacti…
DG-TTA: Out-of-domain Medical Image Segmentation through Augmentation and Descriptor-driven Domain Generalization and Test-Time Adaptation
Christian Weihsbach, Christian N. Kruse, Alexander Bigalke +1
Purpose: Applying pre-trained medical deep learning segmentation models on out-of-domain images often yields predictions of insufficient quality. In this study, we propose to use a…
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…
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…
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…