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 … 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 … 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 … 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 … 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, … 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