Smart Ultrasound Imaging And Perinatal Preterm And Paediatric Image Analysis

This book constitutes the refereed joint proceedings of the First International Workshop on Smart Ultrasound Imaging, SUSI 2019, and the 4th International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2019, held in conjunction with the 22nd International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2019, in Shenzhen, China, in October 2019. The 10 full papers presented at SUSI 2019 and the 10 full papers presented at PIPPI 2019 were carefully reviewed and selected. The SUSI papers cover a wide range of medical applications of B-Mode ultrasound, including cardiac (echocardiography), abdominal (liver), fetal, musculoskeletal, and lung. The PIPPI papers cover the detailed scientific study of volumetric growth, myelination and cortical microstructure, placental structure and function.

This book constitutes the proceedings of the First International Workshop on Advances in Simplifying Medical UltraSound, ASMUS 2020, and the 5th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2020, held in conjunction with MICCAI 2020, the 23rd International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference was planned to take place in Lima, Peru, but changed to an online event due to the Coronavirus pandemic. For ASMUS 2020, 19 contributions were accepted from 26 submissions; the 14 contributions from the PIPPI workshop were carefully reviewed and selected from 21 submissions. The papers were organized in topical sections named: diagnosis and measurement; segmentation, captioning and enhancement; localisation and guidance; robotics and skill assessment, and PIPPI 2020.

​This book constitutes the refereed proceedings of the 8th International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2023, held in conjunction with the 26th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2023, in Vancouver, Canada, in October 2023. The 10 full papers presented at PIPPI 2023 were carefully reviewed and selected from 14 submissions. PIPPI 2023 workshop complements the main MICCAI conference by providing a focused discussion on the challenges of image analysis techniques as applied to the fetal and infant settings.

This book constitutes the refereed joint proceedings of the First International Workshop on Data Driven Treatment Response Assessment, DATRA 2018 and the Third International Workshop on Preterm, Perinatal and Paediatric Image Analysis, PIPPI 2018, held in conjunction with the 21st International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2018, in Granada, Spain, in September 2018. The 5 full papers presented at DATRA 2018 and the 12 full papers presented at PIPPI 2018 were carefully reviewed and selected. The DATRA papers cover a wide range of exploring pattern recognition technologies for tackling clinical issues related to the follow-up analysis of medical data with focus on malignancy progression analysis, computer-aided models of treatment response, and anomaly detection in recovery feedback. The PIPPI papers cover topics of advanced image analysis approaches focused on the analysis of growth and development in the fetal, infant and paediatric period.

Fetal and perinatal medicine is a rapidly expanding field, and noninvasive imaging by means of ultrasonography and MRI is playing a major role in refining diagnosis and therapy. Recent technological advances in these imaging modalities now allow unprecedented morphological depiction of the fetus and excellent insight into complex pathologic conditions, as well as yielding superior guidance for therapeutic fetal inter ventions. I am very pleased that Professor F. Avni , a leading international pediatric radiologist, was prepared to take on the challenging task of preparing and editing this comprehen sive and up-to-date overview of our knowledge in the area of fetal and perinatal imaging. He has been successful in engaging well-known experts with outstanding qualifications in fetal imaging to join him in this venture. I would like to congratulate Professor Avni and all contributing authors most sincerely for their excellent work. I am confident that this outstanding volume will meet with great interest not only from general as well as specialized pediatric radiologists but also from neonatologists and pediatricians. I trust it will enjoy the same success as many previous volumes in this series. ALBERT L. BAERT Leuven Preface Fetal and perinatal medicine would not have developed without the extensive use of obstetric ultrasound (US). In order to be efficient, the examination has to be performed very carefully and by sonologists fully conversant with the normal and abnormal development of the fetus.

Machine Learning and Deep Learning Techniques for Medical Image Recognition comprehensively reviews deep learning-based algorithms in medical image analysis problems including medical image processing. It includes a detailed review of deep learning approaches for semantic object detection and segmentation in medical image computing and large-scale radiology database mining. A particular focus is placed on the application of convolutional neural networks with the theory and varied selection of techniques for semantic segmentation using deep learning principles in medical imaging supported by practical examples. Features: Offers important key aspects in the development and implementation of machine learning and deep learning approaches toward developing prediction tools and models and improving medical diagnosis Teaches how machine learning and deep learning algorithms are applied to a broad range of application areas, including chest X-ray, breast computer-aided detection, lung and chest, microscopy, and pathology Covers common research problems in medical image analysis and their challenges Focuses on aspects of deep learning and machine learning for combating COVID-19 Includes pertinent case studies This book is aimed at researchers and graduate students in computer engineering, artificial intelligence and machine learning, and biomedical imaging.

Artificial intelligence models are being used to make labor and delivery safer for mothers and newborns. Sensors are exploited to gauge health parameters, and machine learning techniques are investigated to predict the health conditions of patients to assist medical practitioners. This is a critical area of study as maternal and infant health are indispensable for a healthy society. Predicting Pregnancy Complications Through Artificial Intelligence and Machine Learning considers the recent advances, challenges, and best practices of artificial intelligence and machine learning in relation to pregnancy complications. Covering key topics such as pregnancy complications, wearable sensors, and healthcare technologies, this premier reference source is ideal for nurses, doctors, computer scientists, medical professionals, industry professionals, researchers, academicians, scholars, instructors, and students.

This book deals with the concept of medical ultrasound imaging and discusses array signal processing in ultrasound. Signal processing using different beamforming techniques in order to achieve a desirable reconstructed image and, consequently, obtain useful information about the imaging medium is the main focus of this book. In this regard, the principles of image reconstruction techniques in ultrasound imaging are fully described, and the required processing steps are completely expanded and analyzed in detail. Simulation results to compare the performance of different beamformers are also included in this book to visualize their differences to the reader. Other advanced techniques in the field of medical ultrasound data processing, as well as their corresponding recent achievements, are also presented in this book. Simply put, in this book, processing of medical ultrasound data from different aspects and acquiring information from them in different manners are covered and organized in different chapters. Before going through the detailed explanation in each chapter, it gives the reader an overview of the considered issue and focuses his\her mind on the challenge ahead. The contents of the book are also presented in such a way that they are easy for the reader to understand. This book is recommended for researchers who study medical ultrasound data processing.