James McCouat

James McCouat

Doctoral Student

Student, St Hugh's College

E: james.mccouat@st-hughs.ox.ac.uk

Themes:

• Artificial Intelligence and Machine Learning
• Computational Biology and Health Informatics

Biography

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?

Github: https://github.com/jfm15

Selected Publications

View AllManage publications

• Automatically Diagnosing Hip Conditions from X−rays using Landmark Detection

  James McCouat‚ Sion Glyn−Jones and Irina Voiculescu

  International Symposium on Biomedical Imaging (ISBI). April, 2021.

  Details about Automatically Diagnosing Hip Conditions from X−rays using Landmark Detection | BibTeX data for Automatically Diagnosing Hip Conditions from X−rays using Landmark Detection

• Vertebrae Detection and Localization in CT with Two−Stage CNNs and Dense Annotations

  James McCouat and Ben Glocker

  2020.

  Details about Vertebrae Detection and Localization in CT with Two−Stage CNNs and Dense Annotations | BibTeX data for Vertebrae Detection and Localization in CT with Two−Stage CNNs and Dense Annotations

Supervisor