Merge branch 'main' of https://github.com/DIAGNijmegen/Literature

diag.bib

@@ -8946,8 +8946,8 @@ @inproceedings{Heri20
   year = {2020},
 }
 
-@article{Heri2021d,
-  author = {{Hering}, A. and {Hansen}, L. and {Mok}, T.~C.~W. and {Chung}, A. and {Siebert}, H. and {H{\"a}ger}, S. and {Lange}, A. and {Kuckertz}, S. and {Heldmann}, S. and {Shao}, W. and others},
+@article{Heri21d,
+  author = {Alessa Hering and Lasse Hansen and Tony C. W. Mok and Albert C. S. Chung and Hanna Siebert and Stephanie Hager and Annkristin Lange and Sven Kuckertz and Stefan Heldmann and Wei Shao and Sulaiman Vesal and Mirabela Rusu and Geoffrey Sonn and Theo Estienne and Maria Vakalopoulou and Luyi Han and Yunzhi Huang and Pew-Thian Yap and Mikael Brudfors and Yael Balbastre and Samuel Joutard and Marc Modat and Gal Lifshitz and Dan Raviv and Jinxin Lv and Qiang Li and Vincent Jaouen and Dimitris Visvikis and Constance Fourcade and Mathieu Rubeaux and Wentao Pan and Zhe Xu and Bailiang Jian and Francesca De Benetti and Marek Wodzinski and Niklas Gunnarsson and Jens Sjolund and Daniel Grzech and Huaqi Qiu and Zeju Li and Alexander Thorley and Jinming Duan and Christoph Grossbrohmer and Andrew Hoopes and Ingerid Reinertsen and Yiming Xiao and Bennett Landman and Yuankai Huo and Keelin Murphy and Nikolas Lessmann and Bram van Ginneken and Adrian V. Dalca and Mattias P. Heinrich},
   title = {Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning},
   abstract = {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, in part because of the   lack of availability of such diverse data. 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 benchmark for comprehensive characterisation of deformable registration algorithms. A continuous evaluation will be possible at \url{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, and the results of further analysis of transferability to new datasets, the importance of label supervision, and resulting bias.},