Simplifying Medical Ultrasound : Second International Workshop, ASMUS 2021, Held in Conjunction with MICCAI 2021, Strasbourg, France, September 27, 2021, Proceedings / edited by J. Alison Noble, Stephen Aylward, Alexander Grimwood, Zhe Min, Su-Lin Lee, Yipeng Hu.

Format:

Book

Contributor:

Noble, J. Alison, Editor.

Aylward, Stephen., Editor.

Grimwood, Alexander., Editor.

Min, Zhe., Editor.

Lee, Su-Lin, Editor.

Hu, Yipeng, Editor.

SpringerLink (Online service)

Series:

Computer Science (SpringerNature-11645)

LNCS sublibrary. Image processing, computer vision, pattern recognition, and graphics ; SL 6, 12967

Image Processing, Computer Vision, Pattern Recognition, and Graphics ; 12967

Language:

English

Subjects (All):

Image processing-Digital techniques.

Computer vision.

Artificial intelligence.

Bioinformatics.

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computational and Systems Biology.

Local Subjects:

Computer Imaging, Vision, Pattern Recognition and Graphics.

Artificial Intelligence.

Computational and Systems Biology.

Physical Description:

1 online resource (XIII, 230 pages) : 77 illustrations, 64 illustrations in color.

Edition:

1st ed. 2021.

Contained In:

Springer Nature eBook

Place of Publication:

Cham : Springer International Publishing : Imprint: Springer, 2021.

System Details:

text file PDF

Summary:

This book constitutes the proceedings of the Second International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2021, held on September 27, 2021, in conjunction with MICCAI 2021, the 24th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference was planned to take place in Strasbourg, France, but changed to an online event due to the Coronavirus pandemic. The 22 papers presented in this book were carefully reviewed and selected from 30 submissions. They were organized in topical sections as follows: segmentation and detection; registration, guidance and robotics; classification and image synthesis; and quality assessment and quantitative imaging.

Contents:

Automatic ultrasound vessel segmentation with deep spatiotemporal context learning

Multimodal continual learning with sonographer eye-tracking in fetal ultrasound

Development and evaluation of intraoperative ultrasound segmentation with negative image frames and multiple observer labels

Automatic tomographic ultrasound imaging sequence extraction of the anal sphincter

Lung Ultrasound Segmentation and Adaptation between COVID-19 and Community-Acquired Pneumonia

An Efficient Tracker for Thyroid Nodule Detection and Tracking during Ultrasound Scanning

TransBridge: A lightweight transformer for left ventricle segmentation in echocardiography

Adversarial Affine Registration for Real-time Intraoperative Registration of 3-D US-US for Brain Shift Correction

Robust ultrasound-to-ultrasound registration for intra-operative brain shift correction with a Siamese neural network

Pose Estimation of 2D Ultrasound Probe from Ultrasound Image Sequences Using CNN and RNN

Evaluation of low-cost hardware alternatives for 3D freehand ultrasound reconstruction in image-guided neurosurgery

Application potential of robot-guided ultrasound during CT-guided interventions

Towards Scale and Position Invariant Task Classification using Normalised Visual Scanpaths in Clinical Fetal Ultrasound

Efficient Echocardiogram View Classification with Sampling-Free Uncertainty Estimation

Contrastive Learning for View Classification of Echocardiograms

Imaging Biomarker Knowledge Transfer for Attention-based Diagnosis of COVID-19 in Lung Ultrasound Videos

Endoscopic ultrasound image synthesis using a cycle-consistent adversarial network

Realistic Ultrasound Image Synthesis for Improved Classification of Liver Disease

Adaptable image quality assessment using meta-reinforcement learning of task amenability

Deep Video Networks for Automatic Assessment of Aortic Stenosis in Echocardiography

Pruning MobileNetV2 for Efficient Implementation of Minimum Variance Beamforming

Automatic fetal gestational age estimation from first trimester scans.

Other Format:

Printed edition:

ISBN:

978-3-030-87583-1

9783030875831

Access Restriction:

Restricted for use by site license.