Learn2Reg: comprehensive multi-task medical image registration challenge, dataset and evaluation in the era of deep learning

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 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.

Unsupervised Optical Flow Using Cost Function Unrolling

Analyzing motion between two consecutive images is one of the fundamental tasks in computer vision. In the lack of labeled data, the loss functions are split into consistency and smoothness, allowing for self-supervised training. This paper focuses on the cost function derivation and presents an unrolling iterative approach, transferring the hard L1 smoothness constraint into a softer multi-layer iterative scheme. More accurate gradients, especially near non-differential positions, improve the network's convergence, providing superior results on tested scenarios. We report state-of-the-art results on both MPI Sintel and KITTI 2015 unsupervised optical flow benchmarks. The provided approach can be used to enhance various architectures and not limited just to the presented pipeline.