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Simplifying Medical Ultrasound: Third International Workshop, ASMUS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings de Stephen Aylward

Simplifying Medical Ultrasound: Third International Workshop, ASMUS 2022, Held in Conjunction with MICCAI 2022, Singapore, September 18, 2022, Proceedings: Lecture Notes in Computer Science, cartea 13565

Editat de Stephen Aylward, J. Alison Noble, Yipeng Hu, Su-Lin Lee, Zachary Baum, Zhe Min
en Limba Engleză Paperback – 17 sep 2022
This book constitutes the proceedings of the Third International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2022, held on September 18, 2022, in conjunction with MICCAI 2022, the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Singapore.
The 18 papers presented in this book were carefully reviewed and selected from 23 submissions. They were organized in topical sections as follows: classification and detection; Segmentation and Reconstruction; and Assessment, Guidance and Robotics.
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.

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Specificații

ISBN-13: 9783031169014
ISBN-10: 3031169018
Pagini: 194
Ilustrații: XI, 194 p. 73 illus., 69 illus. in color.
Dimensiuni: 155 x 235 mm
Greutate: 0.3 kg
Ediția:1st ed. 2022
Editura: Springer International Publishing
Colecția Springer
Seria Lecture Notes in Computer Science

Locul publicării:Cham, Switzerland

Cuprins

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

Textul de pe ultima copertă

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.