Simplifying Medical Ultrasound
Description
This book constitutes the proceedings of the 6th International Workshop on Simplifying Medical Ultrasound, ASMUS 2025, held in conjunction with MICCAI 2025, the 28th International Conference on Medical Image Computing and Computer-Assisted Intervention. The conference took place in Deajeon, South Korea, on September 23, 2025.
The 24 full papers presented in this book were carefully reviewed and selected from 41 submissions. They were organized in topical sections as follows: 3D Reconstruction and Imaging; Registration, Representation and Generation; Image Acquisition, Segmentation and Interpretability; Classification and Measurements.
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Content
.- 3D Reconstruction and Imaging.
.- DualTrack: Sensorless 3D Ultrasound needs Local and Global Context.
.- Modulated INR with Prior Embeddings for Ultrasound Imaging Reconstruction.
.- DiffUS: Differentiable Ultrasound Rendering from Volumetric Imaging.
.- 3D Heart Reconstruction from Sparse Pose-agnostic 2D Echocardiographic Slices.
.- 3D Ultrasound Volume Reconstruction using a CNN-Transformer model and IMU data.
.- Optimization-Based Calibration for Intravascular Ultrasound Volume Reconstruction.
.- Registration, Representation and Generation.
.- Robust rigid MRI-TRUS registration using attention-CNN and ICP.
.- Det-SAMReg: Few-Shot Medical Image Registration using Vision Foundation Models.
.- D.A.R.K.: Dynamic Graphs based Angle-aware Registration of Knee Ultrasound Point Clouds.
.- The Impact of Biomechanical Quantities on PINNs-based Medical Image Registration.
.- VidFuncta: Towards Generalizable Neural Representations for Ultrasound Videos.
.- From Transthoracic to Transesophageal: Cross-Modality Generation using LoRA Diffusion.
.- DiFUSAL: Diffusion-Based Fetal Ultrasound Synthesis with Active Learning.
.- Image acquisition, segmentation and interpretability.
.- Motion-enhanced Cardiac Anatomy Segmentation via an Insertable Temporal Attention Module.
.- UGFNet: Uncertainty-Guided Graph Neural Network with Frequency-Aware Feature Fusion for Breast Ultrasound Segmentation.
.- L-FUSION: Laplacian Fetal Ultrasound Segmentation & Uncertainty Estimation.
.- Diffusion-based Iterative Counterfactual Explanations for Fetal Ultrasound Image Quality Assessment.
.- Guide2Heart: Proximity Guidance for Standard Echocardiographic View.
.- Classification and Measurements.
.- HiProtoNet: Hyperbolic Hierarchy-aware Part Prototypes for Aortic Stenosis Severity Classification.
.- COVID-19 Severity Prediction from Lung Ultrasound via Dynamic Gated Multi-Instance Learning.
.- WiseLVAM: A Novel Framework For Left Ventrical Automatic Measurements.
.- Learning to Stop: Reinforcement Learning for Efficient Patient-Level Echocardiographic Classification.
.- TREAT-Net: Tabular-Referenced Echocardiography Analysis for Acute Coronary Syndrome Treatment Prediction.
.- Anatomically Constrained Transformers for Cardiac Amyloidosis Classification.