6 citations · 7 across the 4 of their papers we have counts for
6 papers
Strategies for Robust Deep Learning Based Deformable Registration
Joel Honkamaa, Pekka Marttinen
Deep learning based deformable registration methods have become popular in recent years. However, their ability to generalize beyond training data distribution can be poor, signifi…
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…
New multimodal similarity measure for image registration via modeling local functional dependence with linear combination of learned basis functions
Joel Honkamaa, Pekka Marttinen
The deformable registration of images of different modalities, essential in many medical imaging applications, remains challenging. The main challenge is developing a robust measur…
SITReg: Multi-resolution architecture for symmetric, inverse consistent, and topology preserving image registration
Joel Honkamaa, Pekka Marttinen
Deep learning has emerged as a strong alternative for classical iterative methods for deformable medical image registration, where the goal is to find a mapping between the coordin…
Deformation equivariant cross-modality image synthesis with paired non-aligned training data
Joel Honkamaa, Umair Khan, Sonja Koivukoski +4
Cross-modality image synthesis is an active research topic with multiple medical clinically relevant applications. Recently, methods allowing training with paired but misaligned da…