Method and system for choroid-scleral segmentation using deep learning with a choroid-scleral layer model
AssigneeCarl Zeiss Meditec AG
InventorsHomayoun Bagherinia, Lars Omlor
Life of this patent
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Abstract
A System/Method/Device for segmenting the choroid-scleral layer from an optical coherent tomography (OCT) volume scan. The present system uses a deep learning machine model based on a neural network that include multiple convolution layers, but no deconvolution layers. Rather, the present neural network is based on a novel architecture based on the discrete cosine transform.
Claims20
A method for segmenting a choroid-scleral layer from optical coherence tomography (OCT) image data of an eye, comprising:
- collecting the OCT image data of the eye from an OCT system;
- submitting the OCT image data to a deep learning machine model based on a neural network, and producing an initial choroid-scleral layer segmentation, the neural network having:
- a contracting path including a plurality of convolution layers followed by a max pooling layer;
- and
- an expanding path following the contracting path, the expanding path including at least one discrete cosine transform (DCT) decoder;
- and
- submitting the initial choroid-scleral layer segmentation to a classification module to identify and exclude, from the initial choroid-scleral layer segmentation, patch regions deemed to have incorrect segmentation and define a final choroid-scleral layer segmentation.
D) A system for segmenting a choroid-scleral layer from an optical coherence tomography (OCT) volume scan image data of an eye, comprising:
- a light source for generating a beam of light;
- optics for directing the light to one or more locations on a sample including the eye;
- a detector for receiving light returning from the sample and generating signals in response thereto;
- and
- a processor operably coupled to the detector and configured to:
- define the volume scan image data from the generated signals,
- submit the volume scan image data to a deep learning machine model based on a neural network, and producing an initial choroid-scleral layer segmentation, wherein the neural network comprises:
- a contracting path including a plurality of convolution layers followed by a max pooling layer;
- and
- an expanding path following the contracting path, the expanding path including at least one discrete cosine transform (DCT) decoder;
- and
- submitting the initial choroid-scleral layer segmentation to a classification module to identify and exclude, from the initial choroid-scleral layer segmentation, patch regions deemed to have incorrect segmentation and define a final choroid-scleral layer segmentation.
Dependencies are inferred from the claim text.