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, … 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 … Continued
Fourth MICCAI Workshop & Challenge on Spine Imaging In this workshop, we are inviting researchers to share and exchange their experience and expertise in spinal imaging and method … Continued
Understanding brain connectivity in a network-theoretic context has shown much promise in recent years. This type of analysis identifies brain organisational principles, bringing a new perspective to neuroscience. At the … Continued
This year we are co-organising the Workshop on Brain Analysis using Connectivity Networks at MICCAI’16 in Athens in the morning of Monday 17/10/2016. Please consider to submit your work until … 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 … 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 … Continued
Finding the needle in the haystack: detection, tracking and registration in biomedical imaging Abstract From centimeter-sized observations visible in endoscopy to nanometer large intra-cellular structures discernible with Electron Microscopes, … Continued
Neural network's for super resolution Abstract The most important parts for using neural network's for super resolution are data, network architecture and the objective functions. In this talk we … Continued
Title: Learning structure in complex data: machine learning for medical image segmentation, registration and shape analysis