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• Intro
• Preface
• Organization
• Contents
• Automatic Segmentation of the Placenta in BOLD MRI Time Series
• 1 Introduction
• 2 Methods
• 2.1 Model
• 2.2 Additive Boundary Loss
• 2.3 Implementation Details
• 3 Model Evaluation
• 3.1 Data
• 3.2 Evaluation
• 3.3 Results
• 4 Discussion and Conclusion
• References
• A Fast Anatomical and Quantitative MRI Fetal Exam at Low Field
• 1 Introduction
• 2 Methods
• 2.1 Evaluation
• 2.2 Analysis
• 3 Results
• 4 Discussion and Conclusions
• References
• Automatic Fetal Fat Quantification from MRI
• 1 Introduction
• 2 Methodology

• 2.1 Semi-automatic Fetal AT Segmentation
• 2.2 Automatic Fetal Fat Segmentation
• 3 Experimental Results
• 3.1 Study 1: Manual and Semi-automatic Observer Variability
• 3.2 Study 2: Automatic Fetal AT Segmentation
• 3.3 Study 3: Analysis of Manual Corrections Following Automatic Segmentation
• 4 Discussion
• 5 Conclusion
• References
• Continuous Longitudinal Fetus Brain Atlas Construction via Implicit Neural Representation
• 1 Introduction
• 2 Method
• 2.1 Pre-train Stage
• 2.2 Refine Stage
• 2.3 Inference Stage
• 3 Experiments
• 3.1 Setup
• 3.2 Results
• 4 Conclusion

• Attention-Driven Multi-channel Deformable Registration of Structural and Microstructural Neonatal Data
• 1 Introduction
• 2 Method
• 3 Results
• 4 Conclusion
• References
• Automated Multi-class Fetal Cardiac Vessel Segmentation in Aortic Arch Anomalies Using T2-Weighted 3D Fetal MRI
• 1 Introduction
• 1.1 Deep Learning Segmentation
• 1.2 Label Propagation
• 1.3 Contribution
• 2 Methods
• 2.1 Data Specifications
• 2.2 Deep Learning Segmentation Framework
• 2.3 Label Propagation
• 2.4 Attention U-Net Segmentation
• 3 Results
• 3.1 Preliminary Network Architecture Experiments

• 3.2 Test Set and Experiments
• 3.3 Quantitative Results
• 3.4 Visual Inspection
• 4 Discussion
• 5 Conclusion
• References
• Segmentation of Periventricular White Matter in Neonatal Brain MRI: Analysis of Brain Maturation in Term and Preterm Cohorts
• 1 Introduction
• 2 Methods
• 2.1 Cohort, Datasets and Preprocessing
• 2.2 Parcellation Map of Periventricular WM ROIs in the Atlas Space
• 2.3 Automated Segmentation of Periventricular WM ROIs
• 2.4 Quantitative Analysis of PWM in Term and Preterm Cohorts
• 3 Results and Discussion

This book constitutes the refereed proceedings of the First International Workshop on Perinatal, Preterm and Paediatric Image Analysis, PIPPI 2022, held in conjunction with the 25th International Conference on Medical Imaging and Computer-Assisted Intervention, MICCAI 2022, in Singapore, Singapore, in September 2021. The 10 full papers and 1 short papers presented at PIPPI 2022 were carefully reviewed and selected from 12 submissions. PIPPI 2022 workshop complements the main MICCAI conference by providing a focused discussion of perinatal and paediatric image analysis, including the application of sophisticated analysis tools to fetal, neonatal and paediatric imaging data.
(source: Nielsen Book Data)

• Remote Intelligent Assisted Diagnosis System for Hepatic Echinococcosis.- Calibrated Bayesian neural networks to estimate gestational age and its uncertainty on fetal brain ultrasound images.- Automatic Optic Nerve Sheath Measurement in Point-of-Care Ultrasound.- Deep Learning for Automatic Spleen Length Measurement in Sickle Cell Disease Patients.- Cross-Device Cross-Anatomy Adaptation Network for Ultrasound Video Analysis.- Guidewire Segmentation in 4D Ultrasound Sequences Using Recurrent Fully Convolutional Networks.- Embedding Weighted Feature Aggregation Network with Domain Knowledge Integration for Breast Ultrasound Image Segmentation.- A Curriculum Learning Based Approach to Captioning Ultrasound Images.- Deep Image Translation for Enhancing Simulated Ultrasound Images.- Localizing 2D Ultrasound Probe from Ultrasound Image Sequences Using Deep Learning for Volume Reconstruction.- Augmented Reality-Based Lung Ultrasound Scanning Guidance.- Multimodality Biomedical Image Registration using Free Point Transformer Networks.- Label Efficient Localization of Fetal Brain Biometry Planes In Ultrasound Through Metric Learning.- Automatic C-plane detection in pelvic oor transperineal volumetric ultrasound.- Unsupervised Cross-domain Image Classification by Distance Metric Guided Feature Alignment.- Dual-Robotic Ultrasound System for In Vivo Prostate Tomography.- IoT-based Remote Control Study of a Robotic Trans-esophageal Ultrasound Probe via LAN and 5G.- Differentiating Operator Skill during Routine Fetal Ultrasound Scanning using Probe Motion Tracking.- Kinematics Data Representations for Skills Assessment in Ultrasound-Guided Needle Insertion.- 3D Fetal Pose Estimation with Adaptive Variance and Conditional Generative Adversarial Network.- Atlas-based segmentation of the human embryo using deep learning with minimal supervision.- Deformable Slice-to-Volume Registration for Reconstruction of Quantitative T2* Placental and Fetal MRI.- A Smartphone-based System for Real-time Early Childhood Caries Diagnosis.- Automated Detection of Congenital Heart Disease in Fetal Ultrasound Screening.- Harmonised segmentation of neonatal brain MRI: a domain adaptation approach.- A multi-task approach using positional information for ultrasound placenta segmentation.- Spontaneous preterm birth prediction using convolutional neural networks.- Multi-Modal Perceptual Adversarial Learning for Longitudinal Prediction of Infant MR Images.- Efficient multi-class fetal brain segmentation in high resolution MRI reconstructions with noisy labels.- Deep Learning Spatial Compounding from Multiple Fetal Head Ultrasound Acquisitions.- Brain volume and neuropsychological differences in extremely preterm adolescents.- Automatic Detection of Neonatal Brain Injury on MRI.- Unbiased atlas construction for neonatal cortical surfaces via unsupervised learning.
• (source: Nielsen Book Data)

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. .
(source: Nielsen Book Data)