Learn2Reg: Comprehensive Multi-Task Medical Image Registration Challenge, Dataset and Evaluation in the Era of Deep Learning
Creators: name: Han, Luyi
name: Siebert, Hanna
name: Huang, Yunzhi
name: Häger, Stephanie
name: Jian, Bailiang
name: Vesal, Sulaiman
name: Sjölund, Jens
name: Visvikis, Dimitris
name: Li, Qiang
name: Großbröhmer, Christoph
name: Dalca, Adrian V.
name: Sonn, Geoffrey
name: Raviv, Dan
name: Murphy, Keelin
name: Brudfors, Mikael
name: Shao, Wei
name: Fourcade, Constance
name: Gunnarsson, Niklas
name: Huo, Yuankai
name: Hoopes, Andrew
name: Kuckertz, Sven
name: Li, Zeju
name: Yap, Pew-Thian
name: Lv, Jinxin
name: Mok, Tony C. W.
name: Lessmann, Nikolas
name: Balbastre, Yaël
name: Rusu, Mirabela
name: Jaouen, Vincent
name: Reinertsen, Ingerid
name: Joutard, Samuel
name: Duan, Jinming
name: Thorley, Alexander
name: Hansen, Lasse
name: van Ginneken, Bram
name: De Benetti, Francesca
name: Grzech, Daniel
name: Pan, Wentao
name: Rubeaux, Mathieu
name: Wodzinski, Marek
name: Xiao, Yiming
name: Qiu, Huaqi
name: Estienne, Théo
name: Xu, Zhe
name: Vakalopoulou, Maria
name: Hering, Alessa
name: Chung, Albert C. S.
name: Heldmann, Stefan
name: Heinrich, Mattias P.
name: Lifshitz, Gal
name: Modat, Marc
name: Lange, Annkristin
name: Landman, Bennett
Publisher: name: Institute of Electrical and Electronics Engineers (IEEE)
Year: 2023

description: <p>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.</p>