Intrapartum Ultrasound

This book constitutes the proceedings of the MICCAI 2025 Grand Challenge on Intrapartum Ultrasound, IUGC 2025, which was held in conjunction with MICCAI 2025, in Daejeon, South Korea, on September 23, 2025.

The 9 full papers 1 short paper and presented in this volume were carefully reviewed and selected from 20 submissions.

They are grouped into the following topics: Top-performing solutions explored semi-supervised frameworks; Self-supervised pretraining; Transformer-based backbones; Adversarial learning; Pseudo-labeling strategies.

Inhalt

.- Noisy Student-Based Self-Training Enhances Landmark Detection in Intrapartum Ultrasound.

.- Unlabeled Data-Driven Fetal Landmark Detection in Intrapartum Ultrasound.

.- SSL-FetalBioNet: Self-Supervised Learning for Automated Angle of Progression Measurement in Intrapartum Ultrasound.

.- DSNT-DeepUNet: A Coordinate Prediction Method for Intrapartum Ultrasound.

.- Adversarially Fine-tuned Self-Supervised Framework for Automated Landmark Detection in Intrapartum Ultrasound.

.- Progressive Semi-supervised Landmark Detection Algorithm for Intrapartum Ultrasound Measurement.

.- Pseudo-label Enhanced TransUNet for Robust Landmark Localization in Intrapartum Ultrasound.

.- A Two-Stage Semi-Supervised Ensemble Framework for Automated Angle of Progression Measurement in Intrapartum Ultrasound.

.- GRM Framework.

.- Heatmap Regression for Automated Angle of Progression Measurement: The Baseline Method for the IUGC2025.