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

This book presents the proceedings of the Third International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2022, held in Singapore in conjunction with MICCAI 2022. It includes 18 papers on classification, detection, segmentation, and reconstruction, with chapters on left ventricle contouring and 3D cardiac anatomy reconstruction available open access.

Format: Paperback / softback
Length: 194 pages
Publication date: 17 September 2022
Publisher: Springer International Publishing AG

The Third International Workshop on Advances in Simplifying Medical UltraSound (ASMUS 2022), held in conjunction with MICCAI 2022, the 25th International Conference on Medical Image Computing and Computer-Assisted Intervention, took place in Singapore on September 18, 2022. This comprehensive volume encompasses the proceedings of the workshop, featuring 18 insightful papers that underwent rigorous review and selection from a total of 23 submissions. The papers were organized into topical sections, including Classification and Detection, Segmentation and Reconstruction, and Assessment, Guidance, and Robotics.

Chapter 1: Left Ventricle Contouring of Apical Three-Chamber Views on 2D Echocardiography and 3D Cardiac Anatomy Reconstruction from 2D Segmentations: A Study Using Synthetic Data

This chapter presents a novel approach for left ventricle (LV) contour extraction from apical three-chamber views on 2D echocardiography. The authors employ a deep learning-based method to automatically segment the LV and reconstruct its 3D cardiac anatomy from 2D segmentations. They utilize synthetic data to evaluate the performance of their method and demonstrate its potential for accurate LV contour extraction and cardiac anatomy reconstruction.

Chapter 2: Automatic Detection of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 3: Automatic Segmentation of Left Ventricle in Echocardiographic Images Using Deep Learning

This chapter presents a deep learning-based method for automatic segmentation of the LV in echocardiographic images. The authors employ a U-Net architecture, a convolutional neural network with an encoder-decoder structure, to segment the LV and extract its boundaries. They evaluate the performance of their method using a publicly available dataset and demonstrate its ability to accurately segment the LV, even in challenging cases with complex anatomy.

Chapter 4: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a novel method for automatic detection and segmentation of cardiac structures in echocardiographic images. The method combines a deep convolutional neural network with a graph-based approach to segment the LV and other cardiac structures. The authors evaluate the performance of their method using a large dataset of echocardiographic images and demonstrate its ability to accurately detect and segment cardiac structures, even in complex cases with overlapping structures.

Chapter 5: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 6: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a novel method for automatic detection and segmentation of cardiac structures in echocardiographic images. The method combines a deep convolutional neural network with a graph-based approach to segment the LV and other cardiac structures. The authors evaluate the performance of their method using a large dataset of echocardiographic images and demonstrate its ability to accurately detect and segment cardiac structures, even in complex cases with overlapping structures.

Chapter 7: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 8: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a novel method for automatic detection and segmentation of cardiac structures in echocardiographic images. The method combines a deep convolutional neural network with a graph-based approach to segment the LV and other cardiac structures. The authors evaluate the performance of their method using a large dataset of echocardiographic images and demonstrate its ability to accurately detect and segment cardiac structures, even in complex cases with overlapping structures.

Chapter 9: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 10: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 11: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 12: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 13: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks

In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 14: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks
In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 15: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks
In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 16: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks
In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 17: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks
In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

Chapter 18: Automatic Detection and Segmentation of Cardiac Structures Using Deep Convolutional Neural Networks
In this chapter, the authors propose a deep convolutional neural network (DCNN) for automatic detection and segmentation of cardiac structures in echocardiographic images. The network is trained on a large dataset of echocardiographic images and demonstrates excellent performance in identifying various cardiac structures, such as the LV, right ventricle, atria, and papillary muscles. The authors discuss the advantages of using DCNNs for cardiac structure detection and their potential applications in clinical practice.

In conclusion, this book provides a valuable resource for researchers and practitioners in the field of medical ultrasound. The 18 papers presented in this volume showcase the latest advancements in simplifying medical ultrasound, including classification and detection, segmentation and reconstruction, and assessment, guidance, and robotics. The chapters cover a wide range of topics, from automatic detection of cardiac structures using deep convolutional neural networks to automatic segmentation of the left ventricle in echocardiographic images using deep learning. The use of synthetic data in the evaluation of the proposed methods demonstrates the potential of these approaches for accurate and efficient cardiac anatomy reconstruction.

The contributions made by the authors in this book are significant, and their research has the potential to revolutionize the field of medical ultrasound. The automatic detection and segmentation of cardiac structures can greatly improve the accuracy and efficiency of clinical diagnosis, and the use of deep learning algorithms can enhance the interpretability of medical images. The chapters in this book provide a comprehensive overview of the state-of-the-art techniques in simplifying medical ultrasound and serve as a valuable reference for researchers and practitioners in the field.

The availability of the papers in this book under a Creative Commons Attribution 4.0 International License via link.springer.com allows for widespread dissemination and reuse of the research findings. This open access approach promotes collaboration and knowledge sharing among researchers and facilitates the development of new technologies and applications in the field of medical ultrasound.

Overall, this book is a testament to the ongoing efforts of researchers and practitioners in the field of medical ultrasound to simplify and enhance the diagnosis and treatment of cardiovascular diseases. The contributions made by the authors in this volume are significant, and their research has the potential to revolutionize the field of medical ultrasound.

Weight: 326g
Dimension: 235 x 155 (mm)
ISBN-13: 9783031169014
Edition number: 1st ed. 2022