Data Driven Treatment Response Assessment and Preterm, Perinatal, and Paediatric Image Analysis

DeepCS: Deep Convolutional Neural Network and SVM based Single Image Super-Resolution.- Automatic Segmentation of Thigh Muscle in Longitudinal 3D T1-Weighted Magnetic Resonance (MR) Images.- Detecting Bone Lesions in Multiple Myeloma Patient Using Transfer Learning.- Quantification of Local Metabolic Tumor Volume Changes by Registering Blended PET-CT Images for Prediction of Pathologic Tumor Response.- Optimizing External Surface Sensor Locations for Respiratory Tumor Motion Prediction.- Segmentation of Fetal Adipose Tissue Using Efficient CNNs for Portable Ultrasound.- Automatic Shadow Detection in 2D Ultrasound Images.- Multi-Channel Groupwise Registration to Construct and Ultrasound-Specific Fetal Brain Atlas.- Investigating Brain Age Deviation in Preterm Infants: A Deep Learning Approach.- Segmentation of Pelvic Vessels in Pediatric MRI Using a Patch-Based Deep Learning Approach.- Multi-View Image Reconstruction: Application to Fetal Ultrasound Compounding.- EchoFusion: Tracking and Reconstruction of Objects in 4D Freehand Ultrasound Imaging Without External Trackers.- Better Feature Matching for Placental Panorama Construction.- Combining Deep Learning and Multi-Atlas Label Fusion for Automated Placenta Segmentation from 3DUS.- LSTM Spatial Co-transformer Networks for Registration of 3D Fetal US and MR Brain Images.- Automatic and Efficient Standard Plane Recognition in Fetal Ultrasound Images via Multi-Scale Dense Networks.- Paediatric Liver Segmentation for Low-Contrast CT Images.<p></p>