I work on using deep learning to extract clinically useful information from medical images. I am jointly supervised by Irina Voiculescu in Computer Science and Sion Glyn-Jones in NDORMS, a department I am also affiliated with. My current project trains on a dataset of hip x-rays but I am interested in obtaining datasets containing other image modalities and body parts.
My specific areas of research are:
- Anatomical landmark detection from medical images, which is the process of automatically spotting points which are inherently clinically useful, in an image. For example, we can diagnose a hip impingement if we can spot the furthest point laterally of the acetabulum socket and see that it extends too far over the femur head.
- Repurposing pre-existing landmark detection networks that were created for the human pose detection challenge, such as stacked hourglass and high-resolution deep network, to the problem of finding anatomical landmarks.
- How problems in medicine can be encoded into a method which uses anatomical landmarks. For example, how do we spot a bump in the femur head by detecting point landmarks? We would like to do this so we can use landmark detection algorithms which have shown good results.
- Creating new measures for doctors to assess a patient. For example, the current alpha-angle measure of the hip does not capture small bumps in the femur head accurately, can we produce a new measure which does? And can it be calculated by computer automatically
Screening Infant Hips: DDH Diagnosis Via Deep Learning
Andrew Stamper‚ Abhinav Singh‚ James McCouat and Irina Voiculescu
In IEEE International Symposium on Biomedical Imaging (ISBI). April, 2023.
Automatic identification of clinical landmarks and calculation of Graf angles in 2D DDH screening ultrasound images: a pilot study
Abhinav Singh‚ James McCouat‚ Irina Voiculescu‚ Daniel Perry and Sandeep Hemmadi
British Orthopaedic Association (BOA) Congress. September, 2022.
Fast and Accurate Automatic Measurements of the Hip from X−rays using AI
James McCouat‚ Irina Voiculescu and Sion Glyn−Jones
AI in Orthopaedics‚ Orthopaedic Research UK (ORUK). September, 2022.