Med-Team – the foundation of any GLI ML project
Diagnostic imaging is an invaluable source of information to doctors. It is safe to say that for many diseases diagnostics and treatment would be impossible without medical imaging.
With the advancement of technology, the number of medical imaging scans has been increasing steeply and currently medical images make up around 90% of all healthcare data (source: https://www.gehealthcare.com/article/beyond-imagingthe-paradox-of-ai-and-medical-imaging-innovation).
The amount of data in medical imaging is growing which puts a strain on radiology specialists and calls for technological solutions to support them in their daily work through data visualization, providing quantitative information on changes, e.g. volume changes, and others.
Since it is our mission to contribute to solving the big problems that afflict people on a global scale for over 15 years, we have been developing solutions related to the analysis of medical images – first as Future Processing Healthcare, now as Graylight Imaging.
One of the most essential processes of medical imaging is image segmentation and we have gained significant expertise in the application of deep learning for this purpose.
Methods of segmentation
There are three types of segmentation methods: manual, semi-automatic, and automatic.
Manual segmentation, normally performed by a professional, is done slice by slice, or in three dimensions by surrounding the ROI or by labelling the voxels of interest.
Semi-automatic methods use algorithms, thanks to which marking of a region of interest is propagated on successive slices.
When it comes to automatic segmentation two methods can be named: learning and non-learning based. At Graylight Imaging we specialize in the first approach – we build models of deep neural networks for hands-free medical image segmentation. Deep learning as a learning-based technique uses complex neural networks to build models that focus on resolving a specific imaging problem.
The neural network is first trained with labelled examples using for example the U-Net architecture. Once the model has been developed it helps to significantly reduce the length of the segmentation process and does not require interaction from the medical imaging professional.
At this point it must be highlighted that training an accurate model requires a lot of labelled data and segmentations that are used for this purpose have to be done using a manual or semi-automatic method.
Enter Graylight Imaging Med-Team
At Graylight Imaging we have gained significant experience in preparing data for machine learning projects. We have solved challenges which proved that
“it is only a machine learning model which has been trained on a very good data set that produces very good results.“
At Graylight Imaging specialists from various fields – physics, machine learning, medicine, electro-radiology, biomedical engineering, software development co-operate and form unique interdisciplinary R&D teams.
Preparing learning and test data for neural networks – the prerequisite of the process, impossible without exceptional patience, precision, spatial vision and teamwork skills – lies in the hands of GLI Med-Team – a group of medical imaging specialists under the supervision of Karol Miszalski-Jamka MD. As a result of their cooperation with the ML team, three-dimensional sophisticated models of anatomical structures with high accuracy can be obtained.
The role of Med-Team
- collaborating on the development of tools to optimize the segmentation of anatomical structures,
- creation of 3D segmentation of cardiovascular organs (e.g. coronary arteries, heart, aorta) using available tools on the basis of CT and/or MRI images,
- analysis and verification of DICOM images,
- creation of databases together with their analysis,
- analysis of image segmentation results obtained using artificial intelligence AI (machine learning) methods.
What it takes to be part of Med-Team
Anyone who joins MedTeam must have an appropriate education which provides a basis for further study of medical and technical topics related to their work. It should provide such skills as knowledge of medical software that they use every day or the basics of anatomy without which it is difficult to move in the world of medical images.
Currently the team led by Karolina Adamowska is comprised of Ewa Grudzińska, Sabina Wolny, Patrycja Purgoł, Katarzyna Widawka, Marlena Witkowska graduates of electro-radiology, biomedical engineering with specialty: biocybernetics, biomechanics holding certificates in CAD design and modeling software. Members of the team hold masters and doctoral degrees and have had previous RnD experience.
Based on medical data Med-Team create 3D models of anatomical structures using advanced, self-developed software. This means that one should have basic knowledge of medical imaging techniques (CT, MRI), be familiar with DICOM standard in medical imaging techniques, have a thorough understanding of anatomy, know 3D modelling software and have a lot of patience. Generated objects must be as accurate as possible because they are then used to train neural networks.
Additionally, their work is based on standards – those resulting from formal requirements for medical devices (operating under QMS), but also requirements resulting from the internal process approach. Working in a diverse GLI team, which is a technical-medical mix poses a challenge of itself.
Working at MedTeam is a pleasure, adventure and challenge all in one, says Marlena Witkowska, Medical Imaging Specialist.
Benefits from Med-Team work
“We work in the field of building extremely advanced technologies. Deep learning is gaining attention and widespread use right now. Everyone knows now that the quality of resulting solution is as good as data used to build it. With that in mind it is very fair to state that work done by our med team, effort and precision they put into it is critical for our overall success – quality of our resulting AI technologies starts with Med Team” Szymon Janota, CEO.
What needs special emphasis is the fact that without the precise and accurate work of the Med Team it would not be possible for the machine learning teams to create deep learning models and for GLI to develop brand new ground-breaking medical image analysis software.
It is the MedTeam that generates ground-truth labels for supervised learning via the challenging process of segmentation, which is costly and time-consuming but indispensable for building the first ML models.
Based on MedTeam work state-of-the-art solutions can be developed that will help improve the clinical workflow, support clinicians and the medical staff in the diagnostic process with objective and repeatable results.
As a result of MedTeam work patients gain the advantage of a faster, non-invasive diagnosis.
Contact us if you have any questions!
See the previous post by Agnieszka Bilska: Women In Science Day at Graylight Imaging