Smart Ultrasound Imaging and Perinatal, Preterm and Paediatric Image Analysis: First International Workshop, Susi 2019, and 4th International Workshop » książka
First Workshop on Smart UltraSound Imaging.- Straight to the point: reinforcement learning for user guidance in ultrasound.- Registration of Untracked 2D Laparoscopic Ultrasound Liver Images to CT using Content-based Retrieval and Kinematic Priors.- Direct Detection and Measurement of Nuchal Translucency with Neural Networks from Ultrasound Images.- Automated left ventricle dimension measurement in 2D cardiac ultrasound via an anatomically meaningful CNN approach.- SPRNet: Automatic Fetal Standard Plane Recognition Network for Ultrasound Images.- Representation Disentanglement for Multi-task Learning with application to Fetal Ultrasound.- Adversarial Learning for Deformable Image Registration: Application to 3D Ultrasound Image Fusion.- Monitoring Achilles tendon healing progress in ultrasound imaging with convolutional neural networks.- Deep Learning-based Pneumothorax Detection in Ultrasound Videos.- Deep Learning Based Minimum Variance Beamforming for Ultrasound Imaging.- 4th Workshop on Perinatal, Preterm and Paediatric Image Analysis.- Estimation of preterm birth markers with U-Net segmentation network.- Investigating Image Registration Impact on Preterm Birth Classification: An Interpretable Deep Learning Approach.- Dual Network Generative Adversarial Networks for Pediatric Echocardiography Segmentation.- Reproducibility of Functional Connectivity Estimates in Motion Corrected Fetal fMRI.- Plug-and-Play Priors for Reconstruction-based Placental Image Registration.- A Longitudinal Study of the Evolution of the Central Sulcus’ Shape in Preterm Infants using Manifold Learning.- Prediction of failure of induction of labor (IOL) from ultrasound images using radioman features.- Longitudinal analysis of fetal MRI in patients with prenatal spina bifida repair.- Quantifying Residual Motion Artifacts in Fetal fMRI Data.- Topology-preserving augmentation for CNN-based segmentation of congenital heart defects from 3D paediatric CMR.