Our AI components, advanced and accurate machine learning algorithms can be embedded into existing solutions supporting or completely automating the process of manual segmentation.
Medical Image Segmentation
Medical image segmentation plays an important role in medical image processing.
It is the first step for image analysis. It aims to detect the object and find its contours. Thanks to segmentation the next steps – measurement and anomaly analysis – are possible.
With the help of innovative AI technology in medicine the entire process can be automated and improved, thus supporting the diagnostic process.
We have over 15 years of experience in medical image processing. We cooperate with Feedback Medical Ltd. and local universities. We have partnerships with top Polish and French clinical centers.
Our algorithms are applied in the process of testing new drugs used in oncology.
We are working on ways to analyze different organs from different types of studies (CT, MRI, DCE-MRI, PET, PET-CT, angio-CT).
Experience has allowed our machine learning experts to create cutting-edge solutions for medical image segmentation using deep learning.
They have developed innovative machine learning algorithms, thanks to which segmentation is:
- Fully automatic or semi-automatic
- Supports the diagnostic process
DIFFERENT TYPES OF EXAMINATIONS
ORGANS AND LESIONS
Machine Learning algorithms
Medical images are full of information that can be extracted from them through advanced AI technology in medicine and used to personalize treatment.
The key to success is artificial intelligence – accurate state-of-the-art, advanced, properly selected or trained, and constantly evolving machine learning algorithms that can be implemented into an existing solution.
- With efficient algorithms, we can perform semi-automated and fully automated segmentation of internal organs and tumors
- Our algorithms segment organs on different types of examinations (CT, MRI, DCE-MRI, PET, PET-CT, angio-CT)
- We have our own algorithms working also in 3D
- We are able to adapt algorithms to work with a specific type of examination
Properly matched to the examination
Trained to work with a specific type of examination
Application of our algorithms:
Detection, localization and classification of tumors
Simulation of flow in the patient’s cardiovascular system based on extracted information (CFD – Computational Fluid Dynamics)
Development of patient-specific models – personalized coronary artery model
AI components in medical image segmentation
As an AI healthcare company, we have created the possibility of using medical image segmentation in the form of components. Our components can be embedded into existing solutions. They help medical products perform faster and with greater accuracy.
We provide medical imaging segmentation algorithms encapsulated in Docker containers. Containers can run as standalone products or as components embedded in a given application.
Medical image segmentation components
We develop components using our medical and technical expertise and extensive experience with AI technology in medicine.
These are solutions created not only by the best developers, but also by the best machine learning researchers:
Automatic medical image segmentation – by training our algorithms on large diverse data sets and intensively tested workflows, we are able to provide robust automatic segmentation that does not require any manual intervention.
Accurate – our medical image segmentations provide optimal analysis results. Their accuracy is best demonstrated by the fact that they are used clinically and, as components of medical devices, have been successfully verified by accredited certification bodies.
Constantly updated and developed – in cooperation with our clinical partners, we continuously improve the quality of our medical image segmentation. As a result, their accuracy is steadily increasing. In addition, by working with clinical experts we are able to deliver manual annotations for machine learning. For us, medical image segmentation is a continuous improvement process.
Easy-to implement and vendor neutral – our medical images segmentations can be accessed in a vendor-neutral manner by other systems.
We understand how important it is to constantly train and retrain the algorithm. Thanks to the cooperation of scientific and medical centers and the feedback culture, we are consistently improving our models.
We keep up to date by participating in conferences, AI competitions and publishing papers. We are active at major radiology conferences — RSNA and ECR — were the effects of our work have been presented.
Tell us about your project and let’s think together how we can contribute to your future achievements.
Ewa Kieczka, Business Line Manager
Let's talk about our MEDICAL IMAGE SEGMENTATION!
Ewa Kieczka, our Business Line Manager, is waiting to talk to you. Just drop her a line and within 24 hours she will reach you back to talk about your goals and needs.
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