Title: Graph-based deformable registration: slice-to-volume mapping and context specific methods Abstract: Image registration methods, which aim at aligning two or more images into one coordinate system, are among the oldest and most widely used algorithms in computer vision. A particular type of registration algorithm, known as graph-based deformable registration, has become popular during the last … Continued

Title: The challenges of developing an active deep-learning platform Abstract: Deep learning is usually linked to Big Data. However, there are several scientific, engineering and medical imaging problems with limited data available (e.g. rare medical conditions). Can Deep Learning prove a useful tool in such cases? COSMONiO is designing NOUS, an active deep learning platform … Continued

Personalized Blood Flow Simulation from an Image-Derived Model: Changing the Paradigm for Cardiovascular Diagnostics Abstract: Coronary heart disease is the leading cause of mortality worldwide, accounting for 1/3 of all global deaths.  Treatment of stable coronary heart disease is typically performed by medication/lifestyle for a lower disease burden or PCI (stenting) for a greater disease  … Continued

Improving clinical application of cardiac diffusion tensor MRI Abstract  Magnetic resonance diffusion tensor imaging (MRDTI), also known as DTI, has emerged as a powerful non-invasive tool for mapping the orientation-dependent microanatomical organization of fibrous organs such as brain white matter and myocardium. To do so, it elegantly relates the self-diffusion of water molecules that undergo Brownian … Continued

Quantitative Ultrasound Imaging for Diagnosis and Intervention: A Machine Learning Approach Abstract: In recent years, quantitative ultrasound has emerged as a promising technology to improve diagnosis, increase the precision of interventions, and automate the acquisition of data specially in point-of- care settings. Advancements in machine learning and computation power have contributed significantly to these developments in quantitative imaging. In … Continued

Segmenting 2D+T ultrasound imaging data in fetus: improving the estimation of biomarkers of risky adaptations Abstract: Approximately 10% of the pregnancies are complicated by growth restriction and 7% of pregnancies are complicated by gestational diabetes mellitus (GDM). In addition, the increasing rate of obesity will likely rise the proportion of pregnancies complicated by both Type … Continued

U-net: Convolutional Networks for Biomedical Image Segmentation Abstract: In this talk I will present our u-net for biomedical image segmentation. The architecture consists of an analysis path and a synthesis path with additional shortcut-connections. The network is trained end-to-end from scratch with a very low number of annotated images per application. The surprisingly simple strategy to … Continued

Quantifying MRI: from reconstruction to application in neuroimaging Abstract: To understand the origin and development of neurological disorders, Magnetic Resonance Imaging (MRI) has proven to provide valuable quantitative measures. In this overview presentation, I will touch upon several topics. Volumetric measures obtained via segmentation find their application in prenatal famine exposure, HIV and ischemic stroke. … Continued

Title: Learning structure in complex data: machine learning for medical image segmentation, registration and shape analysis

Title: MRI of placenta: from pixels to function