Simplifying Medical Ultrasound [electronic resource] : Third International Workshop, ASMUS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings /

Classification and Detection -- Rapid Lung Ultrasound COVID-19 Severity Scoring with Resource-Efficient Deep Feature Extraction -- Spatio-temporal model for EUS video detection of Pancreatic Anatomy Structures -- RL based Unsupervised Video Summarization framework for Ultrasound Imaging -- Prediction of Kidney Transplant Function with Machine Learning from Computational Ultrasound Features -- Differential Learning from Sparse and Noisy Labels for Robust Detection of Clinical Landmarks in Echo Cine Series -- End-to-End Myocardial Infarction Classification from Echocardiographic Scans -- View Classification of Color Doppler Echocardiography via Automatic Alignment between Doppler and B-mode Imaging -- Segmentation and Reconstruction -- AI-enabled Assessment of Cardiac Systolic and Diastolic Function from Echocardiography -- 3D Cardiac Anatomy Reconstruction from 2D Segmentations: a Study using Synthetic Data -- Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography -- Adnexal Mass Segmentation with Ultrasound Data Synthesis -- Self-Knowledge Distillation for First Trimester Ultrasound Saliency Prediction -- A Universal End-to-End Universal Description of Pulse-Echo Ultrasound Image Reconstruction -- Assessment, Guidance and Robotics -- Learning Generalized Non-Rigid Multimodal Biomedical Image Registration from Generic Point Set Data -- Contact force Prediction for a Robotic Transesophageal Ultrasound Probe via Operating Torque Sensing -- Meta-Registration: Learning Test-Time Optimization for Single-Pair Image Registration -- Automatic Quality Assessment of First Trimester Crown-Rump-Length Ultrasound Images -- Towards Multi-Modal Self-Supervised Video and Ultrasound Pose Estimation for Laparoscopic Liver Surgery.

Chapters "Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography" and "3D Cardiac Anatomy Reconstruction from 2D Segmentations: a Study using Synthetic Data" are available open access under a Creative Commons Attribution 4.0 International License via link.springer.com.