collaborators

7 papers

eess.IV2026

Rendering Novel Views of MRI Using 3D Gaussian Splatting

Robin Y. Park, Mark C. Eid, Rhydian Windsor +4

The objective of this paper is to improve radiological gradings measured on MRIs of spines, by resampling scans so that the new view planes are better aligned with the target anato…

cs.CV2026

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…

cs.CV2025

Semi-Supervised 3D Medical Segmentation from 2D Natural Images Pretrained Model

Pak-Hei Yeung, Jayroop Ramesh, Pengfei Lyu +2

This paper explores the transfer of knowledge from general vision models pretrained on 2D natural images to improve 3D medical image segmentation. We focus on the semi-supervised s…

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…

eess.IV2025

UltraGauss: Ultrafast Gaussian Reconstruction of 3D Ultrasound Volumes

Mark C. Eid, Ana I. L. Namburete, João F. Henriques

Ultrasound imaging is widely used due to its safety, affordability, and real-time capabilities, but its 2D interpretation is highly operator-dependent, leading to variability and i…