Project InnerEye open-source deep learning toolkit: Democratizing medical imaging AI
For over a decade, the Project InnerEye team at Microsoft Research Cambridge has been developing state-of-the-art machine learning methods for the automatic, quantitative analysis of three-dimensional medical images. An important application is to assist clinicians for image preparation and planning tasks for radiotherapy cancer treatment. This task involves a radiation oncologist or specialist technician manually examining and marking up dozens of 3D Computed Tomography (CT) image scans. This may take one or more hours currently, depending on the type of cancer. Our research shows that machine learning (ML) can help reduce this burden on clinicians by decreasing the time for doing this task to a few minutes.
Project InnerEye has been working closely with the University of Cambridge and Cambridge University Hospitals NHS Foundation Trust to make progress on this problem through a deep research collaboration. Dr. Raj Jena, Group Leader in machine learning and radiomics in radiotherapy at the University of Cambridge, explains, “The strongest testament to the success of the technology comes in the level of engagement with InnerEye from my busy clinical colleagues. For over 15 years, the promise of automated segmentation of images for radiotherapy