Cost Function Unrolling in Unsupervised Optical Flow.

Steepest descent algorithms, which are commonly used in deep learning, use the gradient as the descent direction, either as-is or after a direction shift using preconditioning. In many scenarios calculating the gradient is numerically hard due to complex or non-differentiable cost functions, specifically next to singular points. This has been commonly overcome by increased DNN model sizes and complexity. In this work we propose a novel mechanism we refer to as Cost Unrolling, for improving the ability of a given DNN model to solve a complex cost function, without modifying its architecture or increasing computational complexity. We focus on the derivation of the Total Variation (TV) smoothness constraint commonly used in unsupervised cost functions. We introduce an iterative differentiable alternative to the TV smoothness constraint, which is demonstrated to produce more stable gradients during training, enable faster convergence and improve the predictions of a given DNN model. We test our method in several tasks, including image denoising and unsupervised optical flow. Replacing the TV smoothness constraint with our loss during DNN training, we report improved results in all tested scenarios. Specifically, our method improves flows predicted at occluded regions, a crucial task by itself, resulting in sharper motion boundaries.

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. 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.