AI-powered tools tackling oesophageal cancer

Felix Zhou read Engineering at St John’s College, Cambridge graduating in 2013. He obtained his PhD at Oxford in machine learning and computer vision working on the analysis of biological motion in microscopy videos. He is currently a postdoctoral fellow in the Lu Lab at the Ludwig Cancer Institute. Felix is addressing two key clinical problems in Oesophageal Cancer (OeCa) using computational methods.

To improve the current poor outcomes experienced by patients with OeCa, research across Oxford is underway to allow earlier detection of  early stage disease, and to address the issue of poor response rates to standard treatments. Felix’s research seeks to:

• Felix is working closely with collaborators in the Big Data Institute and John Radcliffe Hospital, developing AI-powered augmented reality (AR) tools to assist clinicians in accurately detecting and mapping the location of oesophageal tumours in endoscopy. A significant output of this work is a comprehensive image restoration framework for endoscopy frames corrupted by imaging artefacts. To support scientific reproducibility Felix and his colleagues [1] compiled the first comprehensive image dataset, collected from 6 international institutions and [2] established the Endoscopy Artefact Detection Challenge.

• Felix and colleagues are developing personalised treatments to overcome heterogeneity-driven resistance, a common problem with current standard treatments such as chemotherapy. This involves high-throughput drug screens using miniature organs grown from patient biopsies (organoids), and applying timelapse imaging to monitor growth and interaction dynamics. Felix develops computational analyses to characterize the spatio-temporal development of individual organoids in these screens to more specifically assess a tumour’s sensitivity to existing and new drugs. This builds upon a comprehensive motion analysis framework that Felix recently developed called Motion Sensing Superpixels (MOSES) [3] which has already been shown to capture subtle interaction changes between similar epithelial cell lines.

In Oxford Felix collaborates with Sharib Ali, Jens Rittscher (BDI) and Barbara Braden, Adam Bailey and  James East (clinical endoscopists, John Radcliffe Hospital) on the development of computational endoscopy. He works with the lab of Hagan Bailey (Chemistry),  Jens Rittscher, Daniel Ebner (TDI) and Barbara Braden on developing organoid screens for personalised medicine. His work has been funded by the EPSRC and the Ludwig Cancer Institute. The projects Felix is working on have received funding from the EPSRC, the NIHR Oxford Biomedical Research Centre, and CRUK.

Extract: motion tracts of analysed single organoids

Find out more about our research below

Novel imaging device enters first round of development funding programme

Anna Vella is designing CAPULET: a device to increase the accuracy in delivering particle beam radiotherapy in cancer treatment

Oxford spin out influencing patient care world wide

Oxford cancer research spin our Optellum has received FDA clearance for the world’s first AI-powered clinical decision support for early lung cancer diagnosis

Finding extracellular vesicle biomarkers for oesophageal cancer early detection

Prof Deborah Goberdhan’s lab is investigating extracellular vesicles and the proteins they express as potential biomarkers for the progression from Barrett’s Oesophagus to oesophageal cancer

Understanding how cancer arises from infected tissue

Dr Francesco Boccellato is investigating the mechanisms behind the pre-cancerous condition known as atrophic gastritis. This may help to identify those who may have cancer, as well as find new ways to prevent cancer from progressing

Bowel cancer patients going undiagnosed due to COVID distruption

A new study led by the University of Oxford has found that since the first coronavirus lockdown the number of people diagnosed with bowel cancer in England has fallen sharply, with a deficit persisting up to October 2020

Bioengineering the human gut

An interdisciplinary collaboration generates an advanced model of the human gastrointestinal tract with broad applications for disease research and regenerative medicine
Image from an endoscopy video with the detected artefacts highlighted with coloured boxes.

Using AI to improve the quality of endoscopy videos

A multidisciplinary team of researchers has developed a deep-learning framework for improving endoscopy to aid cancer detection.

Using big data in breast cancer research

The Cancer Epidemiology Unit has been using the largest epidemiological data set of its kind to unlock the secrets of breast cancer, what can be done to prevent it, and which women are most likely to develop it

A new FRONTIER for breast cancer

Latest news from FRONTIER, the trial investigating the potential of the radiotracer Fluciclovine in the subtyping and staging of breast cancers


1 Ali, Sharib, et al. “A deep learning framework for quality assessment and restoration in video endoscopy.” arXiv preprint arXiv:1904.07073 (2019).

2 Ali, Sharib, et al. “Endoscopy artifact detection (EAD 2019) challenge dataset.” arXiv preprint arXiv:1905.03209 (2019).

3 Zhou, Felix Y., et al. “Motion sensing superpixels (MOSES) is a systematic computational framework to quantify and discover cellular motion phenotypes.” eLife 8 (2019): e40162.