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20242026
most citedBeyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos

1 citations · 1 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CV20261 cited

Beyond Benchmarks of IUGC: Rethinking Requirements of Deep Learning Methods for Intrapartum Ultrasound Biometry from Fetal Ultrasound Videos

Jieyun Bai, Zihao Zhou, Yitong Tang +60

A substantial proportion (45\%) of maternal deaths, neonatal deaths, and stillbirths occur during the intrapartum phase, with a particularly high burden in low- and middle-income c…

cs.CV2025

Unsupervised Domain Adaptation via Content Alignment for Hippocampus Segmentation

Hoda Kalabizadeh, Ludovica Griffanti, Pak-Hei Yeung +3

Deep learning models for medical image segmentation often struggle when deployed across different datasets due to domain shifts - variations in both image appearance, known as styl…

eess.IV2025

Automated Fetal Biometry Assessment with Deep Ensembles using Sparse-Sampling of 2D Intrapartum Ultrasound Images

Jayroop Ramesh, Valentin Bacher, Mark C. Eid +6

The International Society of Ultrasound advocates Intrapartum Ultrasound (US) Imaging in Obstetrics and Gynecology (ISUOG) to monitor labour progression through changes in fetal he…

cs.CV2025

Exploring Test Time Adaptation for Subcortical Segmentation of the Fetal Brain in 3D Ultrasound

Joshua Omolegan, Pak Hei Yeung, Madeleine K. Wyburd +5

Monitoring the growth of subcortical regions of the fetal brain in ultrasound (US) images can help identify the presence of abnormal development. Manually segmenting these regions…

eess.IV2024

Automated quality assessment using appearance-based simulations and hippocampus segmentation on low-field paediatric brain MR images

Vaanathi Sundaresan, Nicola K Dinsdale

Understanding the structural growth of paediatric brains is a key step in the identification of various neuro-developmental disorders. However, our knowledge is limited by many fac…

eess.IV2024

Geometric Transformation Uncertainty for Improving 3D Fetal Brain Pose Prediction from Freehand 2D Ultrasound Videos

Jayroop Ramesh, Nicola K Dinsdale, the INTERGROWTH-21st Consortium +2

Accurately localizing two-dimensional (2D) ultrasound (US) fetal brain images in the 3D brain, using minimal computational resources, is an important task for automated US analysis…