Reconstruction, segmentation, and analysis of medical images : first international workshops, RAMBO 2016 and HVSMR 2016, held in conjunction with MICCAI 2016, Athens, Greece, October 17, 2016 : revised selected papers

This book constitutes the refereed proceedings of two workshops held at the 19th International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2016, in Athens, Greece, in October 2016: the First International Workshop on Reconstruction and Analysis of Moving Body Organs, RAMBO 2016, and the First International Workshop on Whole-Heart and Great Vessel Segmentation from 3D Cardiovascular MRI in Congenital Heart Disease, HVSMR 2016. The 17 revised regular papers presented in this book were carefully reviewed and selected from a total of 21 submissions. The papers cover following topics: Registration; Reconstruction; Deep learning for heart segmentation; Discrete optimization and probabilistic intensity modeling; Atlas-based strategies; Random forests. Front Matter....Pages I-XI Front Matter....Pages 1-1 Point-Spread-Function-Aware Slice-to-Volume Registration: Application to Upper Abdominal MRI Super-Resolution....Pages 3-13 Motion Correction Using Subpixel Image Registration....Pages 14-23 Incompressible Phase Registration for Motion Estimation from Tagged Magnetic Resonance Images....Pages 24-33 Front Matter....Pages 35-35 Robust Reconstruction of Accelerated Perfusion MRI Using Local and Nonlocal Constraints....Pages 37-47 Graph-Based 3D-Ultrasound Reconstruction of the Liver in the Presence of Respiratory Motion....Pages 48-57 Whole-Heart Single Breath-Hold Cardiac Cine: A Robust Motion-Compensated Compressed Sensing Reconstruction Method....Pages 58-69 Motion Estimated-Compensated Reconstruction with Preserved-Features in Free-Breathing Cardiac MRI....Pages 70-80 Front Matter....Pages 81-81 Recurrent Fully Convolutional Neural Networks for Multi-slice MRI Cardiac Segmentation....Pages 83-94 Dilated Convolutional Neural Networks for Cardiovascular MR Segmentation in Congenital Heart Disease....Pages 95-102 3D FractalNet: Dense Volumetric Segmentation for Cardiovascular MRI Volumes....Pages 103-110 Automatic Whole-Heart Segmentation in Congenital Heart Disease Using Deeply-Supervised 3D FCN....Pages 111-118 Front Matter....Pages 119-119 A GPU Based Diffusion Method for Whole-Heart and Great Vessel Segmentation....Pages 121-128 Fully-Automatic Segmentation of Cardiac Images Using 3-D MRF Model Optimization and Substructures Tracking....Pages 129-136 Front Matter....Pages 137-137 Strengths and Pitfalls of Whole-Heart Atlas-Based Segmentation in Congenital Heart Disease Patients....Pages 139-146 Automated Cardiovascular Segmentation in Patients with Congenital Heart Disease from 3D CMR Scans: Combining Multi-atlases and Level-Sets....Pages 147-155 Front Matter....Pages 157-157 Automatic Heart and Vessel Segmentation Using Random Forests and a Local Phase Guided Level Set Method....Pages 159-164 Total Variation Random Forest: Fully Automatic MRI Segmentation in Congenital Heart Diseases....Pages 165-171 Back Matter....Pages 173-174