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 medical image registration approaches on a wide range of clinically relevant tasks. This limits the development of registration methods, the adoption of research advances into practice, and a fair benchmark across competing approaches. The Learn2Reg challenge addresses these limitations by providing a multi-task medical image registration data set for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at https:// learn2reg.grand-challenge.org. Learn2Reg covers a wide range of anatomies (brain, abdomen, and thorax), modalities (ultrasound, CT, MR), availability of annotations, as well as intra- and inter-patient registration evaluation. We established an easily accessible framework for training and validation of 3D registration methods, which enabled the compilation of results of over 65 individual method submissions from more than 20 unique teams. We used a complementary set of metrics, including robustness, accuracy, plausibility, and runtime, enabling unique insight into the current state-of-the-art of medical image registration. This paper describes datasets, tasks, evaluation methods and results of the challenge, as well as results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias. While no single approach worked best across all tasks, many methodological aspects could be identified that push the performance of medical image registration to new state-of-the-art performance. Furthermore, we demystified the common belief that conventional registration methods have to be much slower than deep-learning-based methods.

Deep-learning-based registration methods emerged as a fast alternative to conventional registration methods. However, these methods often still cannot achieve the same performance as conventional registration methods because they are either limited to small deformation or they fail to handle a superposition of large and small deformations without producing implausible deformation fields with foldings inside. In this paper, we identify important strategies of conventional registration methods for lung registration and successfully developed the deep-learning counterpart. We employ a Gaussian-pyramid-based multilevel framework that can solve the image registration optimization in a coarse-to-fine fashion. Furthermore, we prevent foldings of the deformation field and restrict the determinant of the Jacobian to physiologically meaningful values by combining a volume change penalty with a curvature regularizer in the loss function. Keypoint correspondences are integrated to focus on the alignment of smaller structures. We perform an extensive evaluation to assess the accuracy, the robustness, the plausibility of the estimated deformation fields, and the transferability of our registration approach. We show that it achieves state-of-the-art results on the COPDGene dataset compared to conventional registration method with much shorter execution time. In our experiments on the DIRLab exhale to inhale lung registration, we demonstrate substantial improvements (TRE below 1.2 mm) over other deep learning methods. Our algorithm is publicly available at https://grand-challenge.org/algorithms/deep-learning-based-ct-lung-registration/.

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 these radiological workflows. While numerous benchmarks and challenges address lesion segmentation in specific organs like the liver, kidneys, and lungs, the larger variety of lesion types encountered in clinical practice demands a more universal approach. To address this gap, we introduced the ULS23 benchmark for 3D universal lesion segmentation in chest-abdomen-pelvis CT examinations. The ULS23 training dataset contains 38,693 lesions across this region, including challenging pancreatic, colon and bone lesions. For evaluation purposes, we curated a dataset comprising 775 lesions from 284 patients. Each of these lesions was identified as a target lesion in a clinical context, ensuring diversity and clinical relevance within this dataset. The ULS23 benchmark is publicly accessible at https://uls23.grand-challenge.org, enabling researchers worldwide to assess the performance of their segmentation methods. Furthermore, we have developed and publicly released our baseline semi-supervised 3D lesion segmentation model. This model achieved an average Dice coefficient of 0.703 ± 0.240 on the challenge test set. We invite ongoing submissions to advance the development of future ULS models.

Objective: We aimed to evaluate the zero-shot performance of open-source generative large language models (LLMs) on clinical information extraction from Dutch medical reports using the Diagnostic Report Analysis: General Optimization of NLP (DRAGON) benchmark. Methods: We developed and released the llm_extractinator framework, a scalable, open-source tool for automating information extraction from clinical texts using LLMs. We evaluated 9 multilingual open-source LLMs across 28 tasks in the DRAGON benchmark, covering classification, regression, and named entity recognition (NER). All tasks were performed in a zero-shot setting. Model performance was quantified using task-specific metrics and aggregated into a DRAGON utility score. Additionally, we investigated the effect of in-context translation to English. Results: Llama-3.3-70B achieved the highest utility score (0.760), followed by Phi-4-14B (0.751), Qwen-2.5-14B (0.748), and DeepSeek-R1-14B (0.744). These models outperformed or matched a fine-tuned RoBERTa baseline on 17 of 28 tasks, particularly in regression and structured classification. NER performance was consistently low across all models. Translation to English consistently reduced performance. Discussion: Generative LLMs demonstrated strong zero-shot capabilities on clinical natural language processing tasks involving structured inference. Models around 14B parameters performed well overall, with Llama-3.3-70B leading but at high computational cost. Generative models excelled in regression tasks, but were hindered by token-level output formats for NER. Translation to English reduced performance, emphasizing the need for native language support. Conclusion: Open-source generative LLMs provide a viable zero-shot alternative for clinical information extraction from Dutch medical texts, particularly in low-resource and multilingual settings.