Ben Glocker

TL;DR: An approximated curvature penalty using second-order derivatives defined on the MRF pairwise potentials is proposed and it is demonstrated that the approximated term has similar properties as higher-order approaches (invariance to linear transformations), while the computational efficiency of pairwise models is preserved.

TL;DR: A fully automatic method to find standardized view planes in 3D image acquisitions by employing a multi-scale reinforcement learning (RL) agent framework and extensively evaluate several Deep Q-Network (DQN) based strategies is proposed.

TL;DR: A novel, generic framework, Image-and-Spatial Transformer Networks (ISTNs), to leverage SoI information allowing us to learn new image representations that are optimised for the downstream registration task, which yields highly accurate registration even with very limited training data.

TL;DR: A novel cost function for semi-supervised learning of neural networks that encourages compact clustering of the latent space to facilitate separation and can be easily applied to existing networks to enable an effective use of unlabeled data.

TL;DR: This work proposes a two-stage approach with weighting schemes based on auto-context and spatial atlas priors that can boost the segmentation accuracy of multiple small organs in whole-body MRI scans.