Image registration is a fundamental requirement for many medical applications. In recent years, deep learning approaches for registration have shown to be a promising alternative to conventional methods. However, most learning based methods do not consider the different physical properties of various tissues, which can result in unrealistic deformation in anatomical regions where both deformable tissue and rigid bone is present. In this work, we develop and evaluate deep learning methods for intrapatient CT-MR registration while maintaining rigidity of the bones. Unconstrained and locally constrained registration methods are compared in an unsupervised and weakly-supervised setting. The results show that qualitatively and quantitatively accurate registrations can be obtained.

In clinical practice, as a first approximation, the severity of an abnormality on an image is often determined by measuring its volume. Researchers often first segment this abnormality with a neural network trained by voxel-wise labels and thereafter extract the volume. Instead of this indirect two steps approach, we propose to train neural networks directly using the volumes as image-level label and predict the volume directly. Using image-level labels to train automatic abnormality prediction could decrease the labeling burden for clinical experts, which is both expensive and time consuming. In this report, a neural network that consisted of a segmentation part and an appended regression part was compared with the indirect segmentation approach. It was investigated if networks trained with image-level labels have the same performance of image-level prediction as networks trained with voxel-wise labels. The neural networks were trained on a large local dataset to quantify white matter hyperintensity (WMH) burden from brain MRI, and their performance was evaluated on a held-out test set. Furthermore, generalization properties were compared by applying the trained networks on four independent public datasets. The networks trained with image-level labels achieved volume quantification that was slightly better than their counterpart on the held-out test set. The attention maps of these networks showed that the networks were able to focus on the surroundings of the WMH, and hence learned meaningful image features. Nevertheless, the attention maps were not suitable to achieve a compatible segmentation. In terms of generalization towards external datasets, the advantage of weak labels for volume quantification did not hold as there was no significant difference between the performance of the label types. The results suggest that neural networks optimized with image-level labels were able to directly predict WMH volume as well as neural networks trained with voxel-wise labels. Subsequently, we also studied networks that were optimized on both image-level and voxel-wise labels. Those networks reached a lower performance, which suggested that the tasks and their image features learned were not similar enough.

The amount of personal imagery kept on (mobile) devices is increasing by the day. Analysis and organization of these large collections of data are becoming increasingly important in the field of digital forensics, as they can aid in the search for legal evidence. The grouping of faces based on their identity is an important aspect as it provides an overview of the person in question and their connection with scenes, objects and other people. In this work, we propose a fuzzy approach to the hard partitioning problem of face clustering for the specific field of forensic investigations. We constructed a pipeline consisting of deep models for face detection and feature extraction, a method for transforming the resulting feature vectors to a graph representation and a
graph-based clustering algorithm for the final partitioning. Focusing on the clustering step, we propose to assign face images to identity clusters using confidence values (rather than a hard cutoff) based on the average similarity with images present in the cluster relative to other clusters. Compared to existing methods, the approach is not only fuzzy but also embraces na¨ıve linking, and instead of transitively merging the links it uses a graph-based algorithm to produce the clusters. Furthermore, we propose an adapted version of the MaxMax algorithm because the original method only returned fuzzy results if weights were exactly equal. However, similarities between images are continuous, making it unsuitable for the case of face clustering. Evaluation of the performance on the Labeled Face in the Wild (LFW) dataset and the challenging IARPA JANUS Benchmark B (IJB-B) shows promising results comparable with state-ofthe-art face clustering algorithms.