Back to results
US 12,700,088 B2Granted
Methods and systems for identification of trabecular meshwork or other intraoccular anatomical structures and tissue types
AssigneeThe Regents of the University of California
InventorsKen Y. Lin, Pierre Baldi, Gregor Urban
Life of this patent
Estimating the term…
Abstract
Devices, systems, kits, software and methods for using deep learning for enhanced identification of anatomical structures or tissue types during eye surgery.
Claims20
A system for identifying an anatomical structure or tissue type within an eye of a subject, said system comprising:
- a viewing device for viewing an area within the eye which includes the anatomical structure or tissue type;
- a camera or imaging device;
- an image processor;
- and
- a display device;
- wherein the camera or imaging device acquires an image of the area viewed by the viewing device;
- wherein the image processor uses deep learning to identify the anatomical structure or tissue type on said image;
- and
- wherein the display device displays an enhanced image on which the anatomical structure or tissue type is marked;
- wherein the image processor comprises a non-transitory data storage medium having recorded thereon a deep-learning generative model that outputs said enhanced image on which the anatomical structure or tissue type is marked;
- wherein the deep-learning generative model comprises at least one Convolutional Neural Network (CNN) that has been trained using data augmentation;
- and
- wherein any pixels with missing values after augmentation were populated by replicating the closest pixel values of the image.
10 more elements
A system for identifying an anatomical structure or tissue type within an eye of a subject, said system comprising:
- a viewing device for viewing an area within the eye which includes the anatomical structure or tissue type;
- a camera or imaging device;
- an image processor;
- and
- a display device;
- wherein the camera or imaging device acquires an image of the area viewed by the viewing device;
- wherein the image processor uses deep learning to identify the anatomical structure or tissue type on said image;
- wherein the image processor comprises a non-transitory data storage medium having recorded thereon a deep-learning generative model that outputs said enhanced image on which the anatomical structure or tissue type is marked;
- and
- wherein the deep-learning generative model comprises one or more Convolutional Neural Networks (CNNs) and the display device displays an enhanced image on which the anatomical structure or tissue type is marked and wherein cross-validation was applied during training of the one or more CNNs.
7 more elements
Dependencies are inferred from the claim text.