2D to 3D Reconstruction

In neuroimaging research, 3D image data is the mainstay for representing anatomical details. However, in conventional clinical practice, pre- and post-natal assessments are performed with 2D video or static US images. The sonographers need to interpret the relationships between the 2D views and 3D brain anatomy and mentally reconstruct a 3D image given just the 2D information. Our goal is to develop methods to reconstruct a 3D brain scan from 2D freehand video acquisitions. We foresee that this would have applications in perinatal clinics, and in LMICs.

Sensorless volumetric reconstruction of fetal brain freehand ultrasound scans with deep implicit representation

Yeung et al. Medical Image Analysis (2024)

Learning to map 2D ultrasound images into 3D space with minimal human annotation

Yeung et al. Medical Image Analysis (2021)

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

Ramesh et al. MICCAI (2024)

Adaptive 3D Localization of 2D Freehand Ultrasound Brain Images

Yeung et al. MICCAI (2022)

RapidVol: Rapid Reconstruction of 3D Ultrasound Volumes from Sensorless 2D Scans

Eid et al. ISBI (2025)

Sli2Vol: Annotate a 3D Volume from a Single Slice with Self-supervised Learning

Yeung et al. MICCAI (2021)