Patent ID: 11967079
Assignee: QURE.AI TECHNOLOGIES PRIVATE LIMITED
Field: Computer technology (Electrical engineering)
Classification: CPC G | IPC G

Claim 11:
12. A system (100) for detecting Large Vessel Occlusion (LVO) on a Computational Tomography Angiogram (CTA) automatically using deep learning techniques, wherein the system (100) comprising:
a memory and at least one processor, wherein the processor comprises:
a vascular territory segmentation module configured to:
receive a set of CTA images, wherein each CTA image is a slice of a region of interest (ROI) comprising a bone region and an intracranial volume (ICV) region, and
mark a territory of vascular segments in the ICV region for each slice of the ROI,

an ICV segmentation module configured to process each slice of the ROI by:
removing bones and noise due to veins/capillaries from each slice of the ROI based on the marked territory of the ICV region using a semantic-segmentation based deep learning approach;
upon removal of the bones, applying a Maximum Intensity Projection (MIP) on each slice of the ROI;
combining the processed slices of the ROI to develop a CTA image;
segmenting the developed CTA image into a Middle Cerebral Artery (MCA) region and an Internal Cerebral Artery (ICA) region based on the marked territory of the ICV; and
sending the segmented ICA region to an ICA-LVO classifier;
wherein the ICA-LVO classifier is configured to determine presence of the LVO on the received ICA region by comparing the segmented ICA region with training data of the ICA region using the deep learning techniques;
wherein the training data of the ICA region that is used for training the ICA-LVO classifier is composed of an Internal Carotid Artery (ICA) data with patches of the ICA region; and
report the presence of the LVO on the segmented ICA region to a user based on the determination by the respective MCA-LVO classifier and ICA-LVO classifier.