24 November 2017, MI 03.13.010 Invited Talk by Prof. Michel Defrise This talk introduces the concept and applications of consistency conditions in inverse problems with redundant data. We consider inverse problems modelled by some linear operator or matrix A, such as the Radon transform in tomography. The inverse problem consists in solving an equation Ax=y for x given measured data y. In many applications this equation admits a solution only if the data satisfy a set of equations denoted C(y)=0, referred to as the consistency conditions. If the data are noise free and the operator A accurately models the imaging system, the data are by definition consistent because the “exact” object x satisfies Ax=y, and in that case C(y)=0. In practice however the consistency conditions are not satisfied; they can then be used to estimate some vector of parameters p of the imaging system (typically calibration parameters) by solving C(p, y)=0 for p, where C(y, p) denotes the consistency condition corresponding to the parameter p. After a general introduction to the concept, we will review a variety of examples pertaining to 2D and 3D tomography. Applications will be briefly described.
24 November 2017, MI 01.07.014 PhD defense by Matthias Wieczorek Modern X-ray based imaging enables recording of phase-contrast (refraction) and dark-field (scattering) information. Tomographic Reconstruction of the dark-field signal poses an especially challenging problem, as the scattering within an object depends on its orientation. Within this thesis an abstract software framework for Tomographic Reconstruction as well as a novel method for Anisotropic X-ray Dark-field Tomography will be presented. A first biomedical experiment on a sample of a human cerebellum indicates that this method could provide a complementary imaging modality for imaging nerve fibers.
22 November 2017, MI 01.09.014 PhD defense by Kanishka Sharma In Autosomal Dominant Polycystic Kidney Disease (ADPKD), automated segmentation of kidneys for total kidney volume (TKV) measurement is very challenging due to severe, disease-related alterations in kidney morphology. This PhD? thesis analyzes the applicability and performance of machine learning techniques (Random Forests and Deep Learning) for kidney segmentation in ADPKD. The developed segmentation method based on Deep Learning allows fast and reproducible TKV measurements, demonstrating that machine learning can be successfully used for complex medical image segmentation tasks.
22 November 2017, MI 00.05.035 PhD defense by Shadi Albarqouni Aimed at improving machine learning algorithms by incorporating domain-specific knowledge, we develop a set of mathematical and technical methods that cope with different conditions of data abundance, reliable labels, and class balance. Proposed methods are evaluated for various biomedical applications, in particular, Tomographic Reconstruction and Noise Reduction in Cryo-Electron Tomography, Mitotic figure Detection in Breast Cancer Histology Images, and Depth Perception in Interventional Imaging.