Medical Image Analysis

Medical Image Analysis

Established: January 1, 2010


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).

Our scientific collaborations

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

Some press buzz

  • An interview for BBC Click on machine learning for medical image analysis




Microsoft Research Volume Rendering SDK

May 2012

COM components which provides scalable implementation of real-time volume rendering intended for server-based GPUs. It could also be accessed from .Net clients using the provided Runtime Callable Wrapper (RCW)

Size: 179 MB

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