22 citations · 29 across the 18 of their papers we have counts for
11 papers · 1 filter
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…
TotalRegistrator: Towards a Lightweight Foundation Model for CT Image Registration
Xuan Loc Pham, Gwendolyn Vuurberg, Marjan Doppen +12
Image registration is a fundamental technique in the analysis of longitudinal and multi-phase CT images within clinical practice. However, most existing methods are tailored for si…
Unstable Prompts, Unreliable Segmentations: A Challenge for Longitudinal Lesion Analysis
Niels Rocholl, Ewoud Smit, Mathias Prokop +1
Longitudinal lesion analysis is crucial for oncological care, yet automated tools often struggle with temporal consistency. While universal lesion segmentation models have advanced…
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…
OncoReg: Medical Image Registration for Oncological Challenges
Wiebke Heyer, Yannic Elser, Lennart Berkel +15
In modern cancer research, the vast volume of medical data generated is often underutilised due to challenges related to patient privacy. The OncoReg Challenge addresses this issue…
The ULS23 Challenge: a Baseline Model and Benchmark Dataset for 3D Universal Lesion Segmentation in Computed Tomography
M. J. J. de Grauw, E. Th. Scholten, E. J. Smit +4
Size measurements of tumor manifestations on follow-up CT examinations are crucial for evaluating treatment outcomes in cancer patients. Efficient lesion segmentation can speed up…