We are happy to share a new STARSTEM publication – The Optimisation of Deep Neural Networks for Segmenting Multiple Knee Joint Tissues from MRIs.
Dr Joshua Kaggie and team members from the University of Cambridge recently had this journal article accepted to Computerized Medical Imaging and Graphics. The journal acts as “a source for the exchange of research results concerning algorithmic advances, development, and application of digital imaging in disease detection, diagnosis, intervention, prevention, precision medicine, and population health”.
We asked the team to discuss their work.
What were you aiming to find out in this publication?
We used machine learning to automatically segment multiple knee tissues, including bones, cartilage, muscles, and cruciate ligaments. This will help us enable better quantitative analysis of these tissues in future studies, and use the best machine learning network to do this.
Why is this important?
Tissue segmentation is a time-consuming process, but very useful for obtaining quantitative metrics – such as shape or signal intensity. Machine learning can accelerate this.
While machine learning is increasingly performed for the segmentation of tissues, this work shows that it can be used for many (10) different tissues, with highly varying characteristics. We expect this type of work to be constantly iterated on, although we were the first to show all of these tissues together and tissues like the ACL/PCL.
Describe the methods chosen.
We used several deep learning methods to automatically segment knee tissues – called the U-Net and the GAN, and compared these with different parameters. We wanted to know the best process to perform this measurement, as it can speed up analysis in future sets and patients.
We used publicly available knee datasets (SKI10, ZIB) and local data (AMROA).
The AMROA participants were questioned on their patient experience as they left the MRI system.
What are the next steps?
We would like to apply this to looking at how signal intensities change with the introduction of stem cells or nanoparticles.
You can read the full abstract and download a pre-proof of the article on our publications page.