Abstract: Systems and methods for assessing glaucoma loss using optical coherence topography. One method according to an aspect comprises receiving optical coherence image data and assessing functional glaucoma damage from retinal optical coherence image data. In an aspect, the systems and methods can map regions and layers of the eye to determine structural characteristics to compare to functional characteristics.

Abstract: Systems and methods for assessing glaucoma loss using optical coherence topography. One method according to an aspect comprises receiving optical coherence image data and assessing functional glaucoma damage from retinal optical coherence image data. In an aspect, the systems and methods can map regions and layers of the eye to determine structural characteristics to compare to functional characteristics.

Abstract: A method of identifying an object of interest can comprise obtaining first samples of an intensity distribution of one or more object of interest, obtaining second samples of an intensity distribution of confounder objects, transforming the first and second samples into an appropriate first space, performing dimension reduction on the transformed first and second samples, whereby the dimension reduction of the transformed first and second samples generates an object detector, transforming one or more of the digital images into the first space, performing dimension reduction on the transformed digital images, whereby the dimension reduction of the transformed digital images generates one or more reduced images, classifying one or more pixels of the one or more reduced images based on a comparison with the object detector, and identifying one or more objects of interest from the classified pixels.

Abstract: A method of identifying an object of interest can comprise obtaining first samples of an intensity distribution of one or more object of interest, obtaining second samples of an intensity distribution of confounder objects, transforming the first and second samples into an appropriate first space, performing dimension reduction on the transformed first and second samples, whereby the dimension reduction of the transformed first and second samples generates an object detector, transforming one or more of the digital images into the first space, performing dimension reduction on the transformed digital images, whereby the dimension reduction of the transformed digital images generates one or more reduced images, classifying one or more pixels of the one or more reduced images based on a comparison with the object detector, and identifying one or more objects of interest from the classified pixels.

Abstract: A method of identifying an object of interest can comprise obtaining first samples of an intensity distribution of one or more object of interest, obtaining second samples of an intensity distribution of confounder objects, transforming the first and second samples into an appropriate first space, performing dimension reduction on the transformed first and second samples, whereby the dimension reduction of the transformed first and second samples generates an object detector, transforming one or more of the digital images into the first space, performing dimension reduction on the transformed digital images, whereby the dimension reduction of the transformed digital images generates one or more reduced images, classifying one or more pixels of the one or more reduced images based on a comparison with the object detector, and identifying one or more objects of interest from the classified pixels.

Abstract: Disclosed are systems and methods for image segmentation using convolutional networks. Image data comprising an image hypervolume can be received. The image hypervolume can be provided to a trained convolutional neural network (CNN). The CNN can output a segmentation of the image hypervolume.

Abstract: Provided is a snapshot spectral domain optical coherence tomographer comprising a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.

Abstract: Disclosed are systems and methods for image segmentation using convolutional networks. Image data comprising an image hypervolume can be received. The image hypervolume can be provided to a trained convolutional neural network (CNN). The CNN can output a segmentation of the image hypervolume.

Abstract: Provided is a snapshot spectral domain optical coherence tomographer comprising a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.

Abstract: Systems and methods for assessing glaucoma loss using optical coherence topography. One method according to an aspect comprises receiving optical coherence image data and assessing functional glaucoma damage from retinal optical coherence image data. In an aspect, the systems and methods can map regions and layers of the eye to determine structural characteristics to compare to functional characteristics.

Abstract: Systems and methods for assessing glaucoma loss using optical coherence topography. One method according to an aspect comprises receiving optical coherence image data and assessing functional glaucoma damage from retinal optical coherence image data. In an aspect, the systems and methods can map regions and layers of the eye to determine structural characteristics to compare to functional characteristics.

Abstract: A method of identifying an object of interest can comprise obtaining first samples of an intensity distribution of one or more object of interest, obtaining second samples of an intensity distribution of confounder objects, transforming the first and second samples into an appropriate first space, performing dimension reduction on the transformed first and second samples, whereby the dimension reduction of the transformed first and second samples generates an object detector, transforming one or more of the digital images into the first space, performing dimension reduction on the transformed digital images, whereby the dimension reduction of the transformed digital images generates one or more reduced images, classifying one or more pixels of the one or more reduced images based on a comparison with the object detector, and identifying one or more objects of interest from the classified pixels.

Abstract: A method of identifying an object of interest can comprise obtaining first samples of an intensity distribution of one or more object of interest, obtaining second samples of an intensity distribution of confounder objects, transforming the first and second samples into an appropriate first space, performing dimension reduction on the transformed first and second samples, whereby the dimension reduction of the transformed first and second samples generates an object detector, transforming one or more of the digital images into the first space, performing dimension reduction on the transformed digital images, whereby the dimension reduction of the transformed digital images generates one or more reduced images, classifying one or more pixels of the one or more reduced images based on a comparison with the object detector, and identifying one ore more objects of interest from the classified pixels.

Abstract: Disclosed are systems and methods for image segmentation using convolutional networks. Image data comprising an image hypervolume can be received. The image hypervolume can be provided to a trained convolutional neural network (CNN). The CNN can output a segmentation of the image hypervolume.

Abstract: Provided is a snapshot spectral domain optical coherence tomographer comprising a light source providing a plurality of beamlets; a beam splitter, splitting the plurality of beamlets into a reference arm and a sample arm; a first optical system that projects the sample arm onto multiple locations of a sample; a second optical system for collection of a plurality of reflected sample beamlets; a third optical system projecting the reference arm to a reflecting surface and receiving a plurality of reflected reference beamlets; a parallel interferometer that provides a plurality of interferograms from each of the plurality of sample beamlets with each of the plurality of reference beamlets; an optical image mapper configured to spatially separate the plurality of interferograms; a spectrometer configured to disperse each of the interferograms into its respective spectral components and project each interferogram in parallel; and a photodetector providing photon quantification.

Abstract: An ocular alignment system for aligning a subject's eye with an optical axis of an ocular imaging device comprising one or more guide light and one or more baffle configured to mask the one or more guide light from view of the subject such that the one or more guide light is only visible to the subject when the eye of the subject is aligned with the optical axis of an ocular imaging system.

Abstract: Systems and methods for assessing glaucoma loss using optical coherence topography. One method according to an aspect comprises receiving optical coherence image data and assessing functional glaucoma damage from retinal optical coherence image data. In an aspect, the systems and methods can map regions and layers of the eye to determine structural characteristics to compare to functional characteristics.

Abstract: Systems and methods for assessing glaucoma loss using optical coherence topography. One method according to an aspect comprises receiving optical coherence image data and assessing functional glaucoma damage from retinal optical coherence image data. In an aspect, the systems and methods can map regions and layers of the eye to determine structural characteristics to compare to functional characteristics.

Abstract: An ocular alignment system for aligning a subject's eye with an optical axis of an ocular imaging device comprising one or more guide light and one or more baffle configured to mask the one or more guide light from view of the subject such that the one or more guide light is only visible to the subject when the eye of the subject is aligned with the optical axis of an ocular imaging system.

Abstract: Provided are systems and methods for analyzing images. An exemplary method can comprise receiving at least one image having one or more annotations indicating a feature. The method can comprise generating training images from the at least one image. Each training image can be based on a respective section of the at least one image. The training images can comprise positive images having the feature and negative images without the feature. The method can comprise generating a feature space based on the positive images and the negative images. The method can further comprise identifying the feature in one or more unclassified images based upon the feature space.