Irina Voiculescu

Professor Irina Voiculescu

Associate Professor of Computer Science

Stipendiary Lecturer, Oriel College

E: irina@cs.ox.ac.uk

T: +44 1865 2 83501

Themes:

Completed Projects:

Interests

My research develops machine learning methods for medical image analysis, including segmentation, 3D reconstruction, and anatomical landmark detection. Current projects integrate multi-modality data to improve diagnostic accuracy, while ensuring trust and fairness in healthcare AI.

Research

I lead the Oxford Medical Imaging Science (OxMedIS) group, which works across x-ray, MRI, CT, ultrasound, and other imaging data. We design algorithms that detect and classify clinically relevant features, reconstruct anatomical structures in 3D, and provide tools that balance automation with clinician input. Beyond image analysis, we address failure detection and model calibration to ensure reliability, with the ultimate goal of translating these methods into clinical practice.

Vacancies

Prospective DPhil/PhD candidates who meet the entry requirements are most welcome to apply to join my group.

Postdoctoral positions are advertised on the list of vacancies. Interdisciplinary postoctoral applicants from areas other than Computer Science may check their eligibility for the Schmidt AI Fellowships.

Students taking the MSc, MCompSci and related courses can work with us by choosing or proposing a project in one of the topics below. Regrettably we cannot accommodate internships.

Clinical applications, using different data acquisition modalities

X-ray

Adult hip x-ray — hip/joint angles

Landmark detection x-ray — cephalometric landmarks

Adult hand x-ray — scaphoid bone

Unicompartmental knee x-ray

The key technology behind taking measurements relies on being able to identify anatomical landmarks. In our group we have designed bespoke landmark identification methods, as well as a novel way of displaying them. This work has been validated on a publicly available cephalometric dataset. We are also winners of the 2024 MICCAI landmark detection challenge.

We study hip x-rays to take angle measurements (such as the alpha angle) which are clinically relevant. Our results have been validated extensively on research datasets.

We detect features related to unicompartmental knee replacement and investigate clinical issues around fitting this kind of knee prosthesis.

Ultrasound

Infant DDH — developmental dysplasia angles

Measurements through landmark detection

Recent work has involved the study of ultrasound scans of infant hips, with a view to screening for developmental dysplasia of the hip (DDH). This work has scope for being translated into clinics.

MRI

Hip cartilage MRI segmentation

Knee MRI segmentation

BRATS brain MRI — tumour segmentation

Cardiac MRI segmentation (ACDC dataset)

Semantic segmentation work has been both semi- and fully automated. Our methods have been used to label hip and knee joint bones, and also hip cartilage. We have extensive experience with a variety of MRI protocols. Segmentation can be fully supervised (from masks or contours) or weakly supervised (from scribbles).

CT

Abdominal CT segmentation and 3D reconstruction

Abdominal CT — segmentation & 3D reconstruction

This 3D reconstruction of an abdominal CT scan features the rib cage, liver, kidneys, spleen, blood vessels and a renal tumour. Some of our techniques are capable of identifying kidneys automatically, as well as other organs in CT scans of the abdomen.

We detect features related to unicompartmental knee replacement and investigate clinical issues around fitting this kind of knee prosthesis.

Dermoscopy

We classify skin lesions into multiple, potentially overlapping classes. We calibrate our model to ensure that, when it is confident in its prediction, the prediction is also correct.

Dermatoscopic images — multi-class classification

Other interests

Over the years, my other interests have also included

3D modelling, 3D printing
Multi-dimensional modelling
Constructive Solid Geometry (CSG)
Path planning
Mathematics of curves and surfaces
Finding roots of polynomials (for 3D modelling)
Molecular modelling

Teaching

In the Geometric Modelling course we build interesting 3D models and design spline curves. Recursive dodecahedron

Biography

I have been at the Department of Computer Science at Oxford since 1999. I obtained my PhD at the University of Bath, for research in Constructive Solid Geometry (CSG). I contributed to the development of the geometric modelling software svLis through the application of polynomial root finding methods.

As part of the Spatial Reasoning research group at Oxford I have been conducting research in the areas of molecular modelling (protein docking), medical imaging (CT and MRI scan analysis) and polynomial root finding (interval arithmetic). I am interested in other aspects of geometric modelling such as as applications of multivariate polynomial forms in geometry, Bernstein-form polynomials and generalized Newton-like methods.

I am actively involved in public engagement activities, such as the Oxford RobotGames, a programme where children of high school age design and build their own robot in half a day. Another on-going project of the Spatial Reasoning group has been to build a robot sheepdog which automatically herds either live or robotic animals. The Oxford robotic flock was shown in 2001 at the Royal Society Summer Science Exhibition.

I am Fellow of the UK Geometric Modelling Society and Associate Editor of the SPIE Journal of Medical Imaging.