One of the world's most prestigious medical research and education institutions was pursuing a seismic shift in healthcare: the first fully automatic medical image segmentation software.
AI Dev was eager to leverage the power of deep learning to achieve this breakthrough.
When a patient gets an X-ray, MRI or CT, it's typically to analyze specific organs. However, there are many surrounding anatomical features that obscure the image.
For example, the locations of the heart and liver overlap in CT scan images, making it challenging to segment exactly where the liver ends and the heart begins.
Automatic isolation of organs would be a quantum leap forward, allowing healthcare professionals to analyze and diagnose more effectively, without the distraction of other body parts and background noise.
Convert segmented organs into files for 3D printing precise replicas.
Empower surgeons to practice on exact replicas of a patient's organ before operating.
Commercial software is laborious and limited.
Commercially available software only offered manual segmentation, an extraordinarily slow and labor-intensive process, or "assisted" segmentation that was still tedious and time-consuming.
Critically, commercial software allowed segmentation of the organ as a whole, but struggled to segment the vasculature of the organ - essential for accurate surgical planning.
We set the goal of eliminating the deficiencies of all currently available software by developing one groundbreaking piece of AI capable of overcoming every single one of these limitations.
Achieved accurate automatic "one-click segmentation" enabling instant isolation of organs from complex medical images without manual intervention.
Supported Organs
Developed first-of-its-kind automated system able to segment organs with vasculature and convert to a multi-material 3D print.
Incorporating vasculature into an organ such as the lungs makes a far more useful surgical training tool than lungs without vasculature.
Segmentation accuracy is measured by the Jaccard score — the overlap between the AI's output and the true organ boundary. The closer to 100%, the more precise.
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