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Medical Image Analysis


Our mission. To increase the productivity of doctors and improve patient outcome by applying state of the art machine learning to the automatic analysis of medical images.

Project description. Analysis of medical images is essential in modern medicine. With the ever increasing amount of patient data, new challenges and opportunities arise for different phases of the clinical routine, such as diagnosis, treatment and monitoring.

The InnerEye team focuses on the automatic analysis of patients' medical scans. It uses state of the art machine learning techniques for the:

  • automatic delineation and measurement of healthy anatomy and anomalies;
  • robust registration for monitoring disease progression;
  • semantic navigation and visualization for improved clinical workflow;
  • development of natural user interfaces for medical practitioners.

Some achievements. In Aug 2012 our algorithm for the automatic detection and localization of anatomy within Computed Tomography scans has obtained FDA 510(k) approval (ref. num. K120734).

- An overview of the InnerEye project


Demo videos

- Interactive segmentation of structures in 3D medical images

- Automatic 3D delineation of highly aggressive brain tumours

- Automatic anatomy localization in 3D medical images

- Cloud-based volumetric rendering of medical images

- Automatic localization and identification of vertebrae in 3D CT scans

Our scientific collaborations

Current collaborators include: University of Washington, INRIA Asclepios and Addenbrooke's NHS Hospital in Cambridge, amongst others.

Our work described by one of our radiation oncologist collaborators:

Some press buzz

  • An interview for BBC Click on machine learning for medical image analysis
Javier Alvarez-Valle
Javier Alvarez-Valle

Our scientific publications