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This book constitutes the proceedings of the 5th International Workshop on Simplifying Medical Ultrasound, ASMUS 2024, held in conjunction with MICCAI 2024, the 27th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Marrakesh, Morocco on October 6, 2024.
The 21 full papers presented in this book were carefully reviewed and selected from 34 submissions. They were organized in topical sections as follows: Image Acquisition, Synthesis and Enhancement; Tracking, Registration and Image-guided Interventions; Segmentation; and Classification and Detection.
List of contents
.- Image Acquisition, Synthesis and Enhancement.
.- Unsupervised Physics-Inspired Shear Wave Speed Estimation in Ultrasound Elastography.
.- Simplifying Prostate Elastography Using Micro-Ultrasound and Transfer Function Imaging.
.- Do High-Performance Image-to-Image Translation Networks Enable the Discovery of Radiomic Features? Application to MRI Synthesis from Ultrasound in Prostate Cancer.
.- PHOCUS: Physics-Based Deconvolution for Ultrasound Resolution Enhancement.
.- Tracking, Registration and Image-guided Interventions.
.- PIPsUS: Self-Supervised Point Tracking in Ultrasound.
.- Structure-aware World Model for Probe Guidance via Large-scale Selfsupervised Pre-train.
.- An Evaluation of Low-Cost Hardware on 3D Ultrasound Reconstruction Accuracy.
.- Learning to Match 2D Keypoints Across Preoperative MR and Intraoperative Ultrasound.
.- Automatic facial axes standardization of 3D fetal ultrasound images.
.- Segmentation.
.- C-TRUS: A Novel Dataset and Initial Benchmark For Colon Wall Segmentation in Transabdominal Ultrasound.
.- Label Dropout: Improved Deep Learning Echocardiography Segmentation Using Multiple Datasets With Domain Shift and Partial Labelling.
.- Introducing Anatomical Constraints in Mitral Annulus Segmentation in Transesophageal Echocardiography.
.- Interactive Segmentation Model for Placenta Segmentation from 3D Ultrasound Images.
.- Enhanced Uncertainty Estimation in Ultrasound Image Segmentation with MSU-Net.
.- Classification and Detection.
.- Multi-Site Class-Incremental Learning with Weighted Experts in Echocardiography.
.- Masked autoencoders for medical ultrasound videos using ROI-aware masking.
.- Uncertainty-based Multi-modal Learning for Myocardial Infarction Diagnosis using Echocardiography and Electrocardiograms.
.- Fetal Ultrasound Video Representation Learning using Contrastive Rubik's Cube Recovery.
.- LoRIS - Weakly-supervised Anomaly Detection for Ultrasound Images.
.- Unsupervised Detection of Fetal Brain Anomalies using Denoising Diffusion Models.
.- Diffusion Models for Unsupervised Anomaly Detection in Fetal Brain Ultrasound.
