collaborators

5 papers

cs.CV2026

Segmentation Pre-training for Label-Efficient Lumbar Spine Degeneration Grading

Monzon Maria, Zisserman Andrew, Jutzeler Catherine R. +1

Automated assessment of degenerative pathology in the lumbar spine on magnetic resonance imaging (MRI) requires access to large-scale datasets of expert-annotated radiological grad…

cs.CV2026

Be Indiscrete: The Benefits of Learning Continuous Spine Degeneration Severity Scores

Maria Monzon, Andrew Zisserman, Robin Y. Park +2

Lumbar spine degeneration is a major contributor to chronic low back pain and is routinely assessed on MRI using ordinal grading systems, e.g. normal, mild, moderate, severe. Conse…

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…

eess.IV2024

3D Spine Shape Estimation from Single 2D DXA

Emmanuelle Bourigault, Amir Jamaludin, Andrew Zisserman

Scoliosis is traditionally assessed based solely on 2D lateral deviations, but recent studies have also revealed the importance of other imaging planes in understanding the deforma…

eess.IV2024

Automated Spinal MRI Labelling from Reports Using a Large Language Model

Robin Y. Park, Rhydian Windsor, Amir Jamaludin +1

We propose a general pipeline to automate the extraction of labels from radiology reports using large language models, which we validate on spinal MRI reports. The efficacy of our…