Experience in medical image processing with a strong focus on machine learning. Research interests are concentrated around the design and development of algorithms for processing and analysis of three-dimensional (3D) computed tomography (CT) and magnetic resonance (MR) images. I am also interested in computer vision topics, like segmentation, recognition and reconstruction.
We are constantly looking for students with an interest in medical image analysis, as well as the use of machine learning and computer vision in novel and established clinical and forensic applications. This page lists specific open student projects on a master and bachelor level. Students coming with their own research ideas are also welcome to get in contact! Please be aware that work on students projects is usually not financially covered by our side.
As a consequence of a bacterial, tooth associated infection are very common. Those pathologies are usually located in the surrounding of the root of the teeth. They can vary in diameter from a simple widening of the periodontal space up to several millimetres or more, being completely bone surrounded or perforating the adjacent anatomical borders. Furthermore, they potentially affect each of the around 30 roots of per jaw. The manual location of those frequently requires a large amount of work, depending on the number of investigated teeth and the quality of the data set as well as on the education and experience of the doctor doing exemination. The aim of the project is to train deep convolutional neural networks (DCNN) to automatically recognize all the infected teeth in the 3D Cone Beam Computed Tomography (CBCT) image.
To start answering fundamental questions for understanding how the brain works, we need to look at the brain structure on the cell levels. Reconstruction of cell morphology and building connectivity diagram requires that all instances of neuron cell are segmented. Differently, to semantic segmentation, instance segmentation does not only assign a class label to each pixel of an image but also distinguishes between instances within each class, e.g., each individual cell in an electronic microscopy image gets assigned a unique ID. This work will investigate interesting direction for simultaneous segmentation of all instances by automatically encoding the individual instances as pixel-wise embeddings.
By learning a sequence of actions that maximize the expected reward, deep reinforcement learning (DRL) brought significant performance improvements in many areas including games, robotics, natural language processing, and computer vision. It was DeepMind, a small and little-known company in 2013, that achieved a breakthrough in the world of reinforcement learning as they implemented a system that could learn to play many classic Atari games with human or even superhuman performance. Sill, it was until recently that DRL started to appear also in medical image applications for landmark detection, automatic view planning from 3D MR images, or active breast lesion detection.
Deep convolutional neural networks (DCNN) have recently shown outstanding performance on image classification and object detection tasks due to their powerful multiscale filters. The dominant filters used in building DCNN architectures are only transitionally invariant, which is not optimal when the problem is rotation equivalent, as it is the case in e.g. cells detection and tracking task. Thus, by explicitly encoding the expected rotational invariance of the object in the image, the complexity of the problem is decreased, leading to a reduction in the size of the required model.