Magnetic resonance (MR) imaging allows us to look inside the human body in exquisite detail, making it an important tool for diagnosing diseases such as cancer. However, analysing these images is challenging due to the fidelity of anatomical and patient-specific information present in three dimensions (3D). Medical professionals typically do these analyses by hand, which is a time-consuming, mundane task prone to human error and high variability.

Work by the project team over the last decade has recently enabled their algorithms for automatic 3D MR image analysis of human load bearing joints (e.g. knee) for healthy volunteers (with little to no presence of disease) to be productised with Siemens Healthcare, Germany. However, identifying and accurately highlighting regions of interest (i.e. object segmentation) corresponding to disease and pathologies were not possible at the time, and technologies did not permit having fast runtimes that are highly desirable in the clinic. Recent work by the authors has provided significant solutions for these problems through the use of state-of-the-art artificial intelligence (AI) methods that are much more accurate and identify pathologies with run-times in the order of seconds as opposed to several minutes.

This project will develop Virtual Incision (VI) to enable the accurate segmentation and image-based separation of pathological structures corresponding to disease (with respect to healthy tissue) within 3D MR images using highly advanced AI technologies. Our product will enable large scale studies into diseases such as Osteoarthritis, which is a chronic debilitating disease of many important joints in the human body that has a total economic cost estimated to be US$23 billion/annum globally. The AI algorithms require resources for the models to be thoroughly generalised and validated on large clinical datasets to ensure robustness, as well as complete the necessary software engineering tasks to form a potential product or grants with global leading partners such as Siemens Healthcare.

Project members

Dr Shakes Chandra

Senior Lecturer
School of Information Technology and Electrical Engineering

Associate Professor Craig Engstrom

Associate Professor
School of Human Movement and Nutrition Sciences

Emeritus Professor Stuart Crozier

Emeritus Professor
Faculty of Engineering, Architecture and Information Technology