8 citations · 10 across the 13 of their papers we have counts for
6 papers · 1 filter
Pitfalls of topology-aware image segmentation
Alexander H. Berger, Laurin Lux, Alexander Weers +3
Topological correctness, i.e., the preservation of structural integrity and specific characteristics of shape, is a fundamental requirement for medical imaging tasks, such as neuro…
A Lightweight Optimization Framework for Estimating 3D Brain Tumor Infiltration
Jonas Weidner, Michal Balcerak, Ivan Ezhov +6
Glioblastoma, the most aggressive primary brain tumor, poses a severe clinical challenge due to its diffuse microscopic infiltration, which remains largely undetected on standard M…
Topograph: An efficient Graph-Based Framework for Strictly Topology Preserving Image Segmentation
Laurin Lux, Alexander H. Berger, Alexander Weers +4
Topological correctness plays a critical role in many image segmentation tasks, yet most networks are trained using pixel-wise loss functions, such as Dice, neglecting topological…
Topologically Faithful Multi-class Segmentation in Medical Images
Alexander H. Berger, Nico Stucki, Laurin Lux +6
Topological accuracy in medical image segmentation is a highly important property for downstream applications such as network analysis and flow modeling in vessels or cell counting…
Cross-domain and Cross-dimension Learning for Image-to-Graph Transformers
Alexander H. Berger, Laurin Lux, Suprosanna Shit +5
Direct image-to-graph transformation is a challenging task that involves solving object detection and relationship prediction in a single model. Due to this task's complexity, larg…
A Learnable Prior Improves Inverse Tumor Growth Modeling
Jonas Weidner, Ivan Ezhov, Michal Balcerak +11
Biophysical modeling, particularly involving partial differential equations (PDEs), offers significant potential for tailoring disease treatment protocols to individual patients. H…