Computational approaches to diseases of the central neural system: In this talk I will summarize efforts and experience conducted to develop decision-making support systems aiming at better quantifying diseases of the central neural system. Special emphasis will be given to the field of neuro-oncology as well as neuro-vascular diseases such as ischemic stroke. A second part of the talk will provide insights into different applications, such as radiomics and response assessment in neuro-oncology.

Image-based modelling of tumor growth: Glioma is the most frequent primary brain tumor and extensive neuroimaging protocols are used to evaluate the progression of the disease and the success of a chosen treatment strategy. This gives rise to large and complex multimodal data sets, and extracting diagnostic information across different clinical imaging modalities and along time poses a significant problem when analysing these data. We provide an overview of the state of the art in brain tumor image segmentation, and present a generative model of tumor growth and image observation that describes the tumor evolution at the macroscopic imaging level. Model personalization relies on a forward model of the patho-physiological process adapted to organ geometries together with image likelihood functions, and an efficient Bayesian inference approach. We illustrate the application of the tumor growth model in radiation therapy.

Data Compression with DRain:  Current imaging techniques allow the examination of physical objects at unprecedented resolutions, leading to very large datasets. DRain is a software technology that enables applications to inspect and visualize a 100+ GB dataset at interactive rates, running on supercomputers as well as on thin clients and notebooks. DRain leverages the diverging gap between the I/O bandwidth and the peak performance of contemporary CPUs. A single-byte I/O transfer can hide in the order of thousands of floating point operations. This “free budget of FLOPs” is employed to decorrelate data by performing a 3D wavelet transform. Special second-generation wavelets and a wavelet-aware codec have been designed to achieve very competitive compression rates while accurately preserving the signal at coarser resolutions. After the lossless data compression, DRain is able to supply dataset previews to the applications at unrivaled performance-accuracy points. The generated previews are of incremental fidelity and eventually reproduce the exact dataset.

Dr. Diego Rossinelli, R&D at Lucid Concepts AG

Optimal segmentation models for fluorescence microscopy images: Detecting and segmenting objects in fluorescence microscopy images becomes more accurate and robust by including prior knowledge about the labeled objects and about how the images have been acquired. This is the basis of model-based image analysis. But how accurately is one possibly able to segment an object, given the information available in an image? Which algorithms extract the maximum amount of information from an image? This is the topic of optimal image analysis. We show information-theoretic bounds for segmentation of extended objects and filaments, and propose model-based algorithms that asymptotically reach these bounds. This enables both user-friendly and optimal segmentation, as implemented in the open-source software “MOSAICsuite” for Fiji and ImageJ.

Extracting structure from large-scale neural recordings: As the size and complexity of neural datasets continue to grow, there is an increasing need for scalable approaches to extract knowledge from these data. In this talk, I will discuss my recent work in developing methods to uncover the structure of neural systems from both X-ray microtomography and serial two photon tomography datasets. My aim is to provide an introduction to a number of modern neural datasets and discuss some of the challenges that we now face in developing learning algorithms for these data.