Oklahoma Patent of the Month – January 2023
Traditional image processing systems have advanced the medical industry and our understanding of a patient’s status. Some solutions can even readily segment certain anatomy, like bones, for in-depth review. Unfortunately, there has not been a solution for autonomously segmenting coronary artery vessel x-ray images.
Kardiolytics Inc, a leader in medical imaging technology, has developed a novel solution for the efficient and autonomous segmentation of contrast-filled coronary artery vessels. The method uses advanced machine learning technologies such as a convolutional neural network (CNN) to process x-ray images, enabling a detailed segmentation of anatomy fragments in a matter of seconds.
The process begins with receiving an x-ray angiography scan of the coronary vessels, which is then preprocessed to output a preprocessed scan. The preprocessing step includes procedures such as windowing, filtering, and normalization, and may even include computing a Jerman filter response, which is then provided as input to a second channel of the segmentation convolutional neural network.
The trained CNN then performs autonomous coronary vessel segmentation and outputs a mask that denotes the coronary vessels. The result is a highly efficient and accurate representation of the coronary vessels, facilitating the diagnosis, treatment, and monitoring of coronary artery diseases.
Prior to Kardiolytics, physicians were reliant on complex, expensive, and highly invasive procedures that involve inserting probes into the heart to obtain the same diagnostic information that can now be readily derived from non-invasive imaging and AI processing. Kardiolytics’ solution is a significant advancement in medical imaging technology, offering a faster and more reliable way to detect and segment contrast-filled coronary artery vessels in x-ray images. With this new technology, medical professionals can diagnose and treat coronary artery diseases with greater ease and accuracy, ultimately improving patient outcomes.
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