Titlebook: Medical Image Computing and Computer Assisted Intervention ? MICCAI 2017; 20th International C Maxime Descoteaux,Lena Maier-Hein,Simon Duch

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Prediction of Brain Network Age and Factors of?Delayed Maturation in Very Preterm Infantsained on a normative subset (77 scans) of our preterm infant cohort dataset of 168 dMRI scans. We found that a random forest regressor could predict preterm infants’ ages to within an average of . weeks. We validate our approach by analysing the correlation between RBNMI and a set of demographic, di

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A 3D Femoral Head Coverage Metric for Enhanced Reliability in Diagnosing Hip Dysplasiaemoral heads agree reasonably well with manually segmented femoral heads (mean dice coefficient of 0.71), with a significant reduction in variability of the associated metric relative to the existing manual 2D-based FHC ratio (. reduction, .). Our findings suggest that the proposed 3D dysplasia metr

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Adaptable Landmark Localisation: Applying Model Transfer Learning to a Shape Model Matching System a cephalometric SMM system to replicate the annotation style of a new observer..Our results demonstrate that tuning an existing system leads to significant improvements in performance on new data, up?to the extent of performing a well as a system that was fully rebuilt using samples from the new da

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0302-9743 tions; interventional imaging and navigation; and medical image computing. Part III: feature .extraction and classification techniques; and machine learning in medical image computing.978-3-319-66181-0978-3-319-66182-7Series ISSN 0302-9743 Series E-ISSN 1611-3349

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Conference proceedings 2017essel analysis; motion and cardiac analysis; tumor processing; planning and simulation for medical interventions; interventional imaging and navigation; and medical image computing. Part III: feature .extraction and classification techniques; and machine learning in medical image computing.

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ntaminants, toxins and bacterial pathogens in food samples in the field continues. In recent years, various sensor technologies have been developed (electrochemical sensors [2], piezoelectric sensors [3], electrical impedance sensors [4], optical sensors [5]) and tested for detection of analytes imp

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ntaminants, toxins and bacterial pathogens in food samples in the field continues. In recent years, various sensor technologies have been developed (electrochemical sensors [2], piezoelectric sensors [3], electrical impedance sensors [4], optical sensors [5]) and tested for detection of analytes imp

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