BACKGROUND

The present disclosure relates to quantitative analysis of physiological images with deep learning algorithms and artificial intelligence. More particularly, the disclosure relates to identification and analysis of ophthalmic tissue in optical coherence tomography (OCT) images and volumes, and two-dimensional (2D) images and three-dimensional (3D) volumes from other imaging sources.

Current OCT systems (such as those using light sources in the 1 μm spectral range region) enable visualization of the different tissue layers in the retina. However, despite such advancements in OCT technology, quantitative analysis of the different layers is primarily based on segmentation techniques that rely on boundary detection within a cross sectional image (B-scan or B-frame).

For example, one common boundary detection method utilizes graph-search theory. But this can be particularly challenging between some layers such as the choroid and sclera, and between the ganglion cell layer (GCL) and the inner plexiform layer (IPL) due to the lack of well-defined physiological boundaries and/or the transparency of those layers. Other common methods suffer from similar deficiencies. Additionally, some approaches use a series of complex steps, such as building 3D models of the blood vessels to identify the choroid-sclera interface (CSI). The CSI is then constructed with a ‘thin plate spline’ (TPS) approach. But this assumes the CSI to be continuous and smooth, while biologically, there may be no clear physical boundary between the two layers.

BRIEF SUMMARY

According to a first example of the subject matter described herein, a method comprises training a machine learning system with at least two training images, a first of the training images being obtained from a first type of physiological tissue and a second of the training images being obtained from a second type of physiological tissue, the machine learning system being trained to recognize differences in the training images between the first and second types of physiological tissues; supplying the trained machine learning system with an image of a subject physiological tissue; with the trained machine learning system, identifying probabilities that pixels in the image belong to the first type of physiological tissue and/or the second type of physiological tissue, each probability corresponding to a pixel of the image; and based on the identified probabilities, identifying a boundary in the image between the first and second types of physiological tissues, or determining a property of the first or second type of physiological tissue.

According to various embodiments of the above example, the first type of physiological tissue and the second type of physiological tissue are different layers of a retina; the first type of physiological tissue is a choroid and the second type of physiological tissue is a sclera; the training images are 2D en face images; the 2D en face images are generated by flattening volumetric imaging data with respect to a reference layer; the reference layer is the Bruch's membrane; the 2D en face images are separated by a predetermined depth; the first training image is from a first 3D volume of training images and the second training image is from a second 3D volume of training images, a center of first 3D volume being a predetermined number of pixels from a center of the second 3D volume; the method further comprises generating a probability map for the pixels in the image supplied to the model, each pixel of the probability map representing the identified probability of a corresponding pixel of the image supplied to the model; the method further comprises comparing the identified probabilities for pixels in an A-line of the image to a predetermined threshold; a boundary pixel in the A-line is identified as the first pixel whose probability is equal to or greater than the predetermined threshold, the boundary pixel being a pixel of the identified boundary; a boundary pixel in the A-line is identified as the first pixel in a set of pixels, each pixel in the set of pixels having an identified probability that is equal to or greater than the predetermined threshold; the boundary in the image is identified according to a shortest path search technique; the boundary in the image is identified according to a machine learning technique; the method further comprises extracting each pixel in the image of the subject that is equal to or greater than a predetermine threshold; the extracted pixels form a 3D volume and the method further comprises displaying the 3D volume formed by the extracted pixels; the image is part of a 3D volume of images, a plurality of images of the 3D volume being supplied to the trained machine learning system and having a boundary between the first and second types of physiological tissues identified or a property of the first and second types of physiological tissues determined; the differences between the first and second training images are textural differences between the first and second types of physiological tissues; the method further comprises preprocessing the first or second training image prior to the machine learning system being trained; and/or the method further comprises preprocessing the image of the subject physiological tissue prior to supplying the trained machine learning system with the image.

