Abstract: Methods are disclosed for the generation and editing of layer delineations within three-dimensional tomography scans. Cross sections of a subject are generated and presented to an operator, who has the ability to edit layer delineations within the cross section, or determine parameters used to generate new cross sections. By guiding an operator through a set of displayed cross sections, the methods can allow for a more rapid, efficient, and error-free segmentation of the subject. The cross sections can be nonplanar in shape or planar and non-axis-aligned. The cross sections can be restricted to exclude one or more user-defined regions of the subject, or to include only one or more user-defined regions of the subject. The cross sections can be localized to a point-of-interest. Iterative implementations of the methods can be used to arrive at a segmentation deemed satisfactory by the user.

Abstract: Methods are disclosed for the generation and editing of layer delineations within three-dimensional tomography scans. Cross sections of a subject are generated and presented to an operator, who has the ability to edit layer delineations within the cross section, or determine parameters used to generate new cross sections. By guiding an operator through a set of displayed cross sections, the methods can allow for a more rapid, efficient, and error-free segmentation of the subject. The cross sections can be nonplanar in shape or planar and non-axis-aligned. The cross sections can be restricted to exclude one or more user-defined regions of the subject, or to include only one or more user-defined regions of the subject. The cross sections can be localized to a point-of-interest. Iterative implementations of the methods can be used to arrive at a segmentation deemed satisfactory by the user.

Abstract: This disclosure relates to a method for automating segmentation of corneal nerve fibers based on a deep learning approach to segmentation. Methods of the invention offer more robust results by utilizing the power of supervised learning methods in concert with the pre- and post processing techniques documented.

Abstract: Methods are disclosed for the generation and editing of layer delineations within three-dimensional tomography scans. Cross sections of a subject are generated and presented to an operator, who has the ability to edit layer delineations within the cross section, or determine parameters used to generate new cross sections. By guiding an operator through a set of displayed cross sections, the methods can allow for a more rapid, efficient, and error-free segmentation of the subject. The cross sections can be nonplanar in shape or planar and non-axis-aligned. The cross sections can be restricted to exclude one or more user-defined regions of the subject, or to include only one or more user-defined regions of the subject. The cross sections can be localized to a point-of-interest. Iterative implementations of the methods can be used to arrive at a segmentation deemed satisfactory by the user.

Abstract: The present invention is directed to a software algorithm that measures the number of corneal nerve fibers in images captured by microscopy including images from patients obtained by in vivo corneal confocal microscopy, a noninvasive technique. The present invention solves a complicated segmentation problem, by exploiting the piece wise linear nature of the nerve fibers—i.e., the nerves are made up of a lot of straight line segments. The image is split into sub-regions, where each sub-region contains nerves mostly running in the same, straight direction. Having the nerves all in straight-lines within a single 2d image region dramatically simplifies the segmentation problem. The image intensities are summed in the direction of the nerves to reduce the 2d representation to a 1d signal having pronounced peaks where the nerves are located.

Abstract: A method is provided that includes identifying a plurality of data sets, each data set is associated with a distribution model and each data set is associated with an image having a first noise level. The method includes partitioning the data sets into a plurality of groups and generating a best representative estimate for each group, the estimate is associated with a second noise level that is less than the first noise level. The method further includes annotating each group and receiving an input data set. The method includes assigning the input data set to a group and annotating the input data set according to that group's annotation.

Abstract: A method is provided that includes identifying a plurality of data sets, each data set is associated with a distribution model and each data set is associated with an image having a first noise level. The method includes partitioning the data sets into a plurality of groups and generating a best representative estimate for each group, the estimate is associated with a second noise level that is less than the first noise level. The method further includes annotating each group and receiving an input data set. The method includes assigning the input data set to a group and annotating the input data set according to that group's annotation.