Emmanuelle Bourigault

I am a PhD student in Computer Vision in the department of Engineering, University of Oxford. My work consists of developping computer models for the automated assessment of scoliosis and spinal deformities. My interests lie in image segmentation, computational geometry, domain adaptation and uncertainty.


PhD Project

  • Development of a pipeline to automatically measure scoliosis on DXA scans and extension to MRIs
  • Multi-modality comparison of curvature and analysis of spine deformities

October 2021 - Present


MIDL 2022
Scoliosis Measurement on DXA Scans Using a Combined Deep Learning and Spinal Geometry Approach Improvements to an automatic method to measure scoliosis.

  • Contributions:

  • Pseudo-labelling the segmentation to handle the domain gap between datasets and a piecewise cubic spline to approximate the predicted spinal midpoints and handle the noise

Click here to see publication
July 2022
MICCAI HECKTOR Challenge September 2021

Automatic Head and Neck Tumor Segmentation and Outcome Prediction in PET/CT Images Developped an automatic method to segment the primary gross target volume on fluoro-deoxyglucose (FDG)-PET and Computed Tomography (CT) images and prediction of progression-free survival in H&N oropharyngeal cancer.

  • Segmentation Task:

  • A new network based on an encoder/decoder architecture with attention mechanisms and full inter- and intra-skip connections inspired from UNet3+ to take advantage of low-level and high-level semantics at full scales is proposed.
    For post-processing, we used Conditional Random Fields (CRF) to refine the predicted segmentation maps.

  • Prediction of patient progression free survival:

  • A Cox proportional hazard regression with relevant clinical, radiomic, and deep learning features was our best performing model.

Click here to see publication
March 2022


University of Oxford

PhD candidate in the VGG lab, department of Engineering - Centre for Doctoral Training, EPSRC in Sustainable Approaches to Biomedical Science: Responsible and Reproducible Research.
September 2020 - September 2024

Imperial College, Engineering Department

Master of Science
Brain Imaging and Computational Neuroscience


Machine Learning, Programming Python, Data Pre-Processing and Analysis (MRI, fMRI, EEG), Neuropsychology, Pharmacology and Neurodegenerative Diseases.

MSc Research Project

A deep learning approach using graph neural networks for axon tracking in 2P-microscopy images, supervised by Dr. Anil Anthony Bharath, BICI lab.

September 2019 - September 2020

University College London (UCL)

BSc Natural Sciences (Mathematics/Statistics & Neuroscience), Faculty of Mathematics and Physical Sciences.
September 2016 - September 2019


Programming Languages & Tools
  • Python
  • C++
  • Matlab
  • Mathematica
  • R
  • BioLinux
  • HTML
  • Excel
  • Office

  • Work ethics and personal skills
    • Responsive work, tested and reproducible
    • Capacity of adaptation and flexibility
    • Motivation and initiative
    • Team work


    • Best CDT 3 months research project 2021
    • Laidlaw Scholarship (2017-2019)