System and method for tissue differentiation in imaging
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Publication Number
US-10359271-B2
Publication Date
2019-07-23
Expiration Date
Abstract
Various embodiments of systems and methods are described herein for obtaining wide field OCT images and other types of image data from at least one portion of a sample. Various embodiments of systems, and methods are described herein for assessing a degree of differentiation for a second region of an OCT image of a tissue sample with respect to another first. Various embodiments of a sample container are also described herein for containing a tissue sample and maintaining the tissue sample in a defined orientation during imaging or handling.
Core Innovation
The invention assesses a degree of differentiation for a second region of a 3D OCT image of a tissue sample with respect to a first region by identifying a boundary in the OCT image to identify the second region disposed below the boundary and identifying first and second sets of OCT image data corresponding to the first region and the second region. For each region, optical datasets are generated based on measurements of at least one optical characteristic.
The method determines a margin width in B-scan data corresponding to a shortest distance between the boundary and a surface of the tissue sample and determines when the margin width is less than a margin threshold. A known optical dataset generated from known OCT image data corresponding to a known region in a stored OCT image is received, and a discrimination score is determined based on the second optical dataset and the known optical dataset.
The discrimination score is compared to a predefined threshold to identify a high degree of difference that indicates different tissue types. The system includes a data interface, a user interface, and a processing module configured to perform boundary identification, region separation, optical dataset generation, margin width evaluation, and discrimination scoring.
Claims Coverage
The independent claims collectively cover a method, a system, and a non-transitory computer-readable medium for assessing a degree of differentiation between a second region and a first or known region in a 3D OCT image. Across these independent claims, the core inventive features focus on boundary-based region identification, optical dataset generation from measured optical characteristics, margin width evaluation, and discrimination scoring against a known optical dataset using predefined thresholds.
Boundary-based region identification in a 3D OCT image
Identifying a boundary in the OCT image to identify the second region in the OCT image, the second region being disposed below the boundary; identifying a first set of OCT image data corresponding to the first region and a second set of OCT image data corresponding to the second region.
Optical dataset generation from measured optical characteristics
Generating a first optical dataset based on measurements of at least one first optical characteristic for the first set of OCT image data; generating a second optical dataset based on measurements of the at least one first optical characteristic for the second set of OCT image data.
Margin width determination relative to a tissue surface
Determining a margin width in B-scan data for the OCT image, the margin width corresponding to a shortest distance between the boundary and a surface of the tissue sample; determining when the margin width is less than a margin threshold.
Discrimination score comparison to a predefined threshold using a known optical dataset
Receiving a known optical dataset generated based on measurements of the at least one first optical characteristic for a known set of OCT image data corresponding to a known region in a stored OCT image; determining a discrimination score based on the second optical dataset and the known optical dataset to indicate a degree of difference between the second region and the known region and comparing the discrimination score to a predefined threshold to identify a high degree of difference that indicates different tissue types.
System architecture with processing module for differentiation assessment
A system comprising a data interface configured to receive OCT image data for the OCT image, a user interface configured to receive at least one input from a user and to display at least one output to the user, and a processing module coupled to the data interface and the user interface, the processing module being configured to identify a boundary; identify first and second sets of OCT image data; generate first and second optical datasets; determine margin width and compare to a margin threshold; receive a known optical dataset; determine a discrimination score and compare it to a predefined threshold to indicate different tissue types.
Non-transitory computer-readable medium implementing differentiation assessment
A non-transitory computer-readable medium storing computer-executable instructions for causing a processor to perform a method of assessing a degree of differentiation for a second region of an OCT image of a tissue sample with respect to a first region, including receiving OCT image data for a 3D OCT image, identifying a boundary and region datasets, generating optical datasets, determining margin width versus a margin threshold, receiving a known optical dataset, and determining and threshold-comparing a discrimination score to indicate different tissue types.
Across the independent claims, the differentiation assessment is centered on detecting a boundary in a 3D OCT image to define a second region, generating optical datasets from measured optical characteristics for both the first and second regions, using a margin width criterion based on the distance between the boundary and a tissue surface, and computing a discrimination score relative to a known optical dataset and comparing the score to a predefined threshold to identify different tissue types. The system and medium claims implement the same functional workflow using the claimed interfaces and processing configuration.
Stated Advantages
Identifies a high degree of difference between the second region and the known region which indicates that the second and known region comprise different tissue types.
Documented Applications
Assessing a degree of differentiation for a second region of a 3D OCT image of a tissue sample with respect to a first region or known region by boundary identification, optical dataset generation, and discrimination scoring.
Correlation of OCT reconstructions with histopathology in rat tumor models and freshly excised human breast lumpectomy specimens.
Imaging workflow for a lumpectomy specimen including a sample container design to maintain tissue orientation and support imaging workflow.
Industrial OCT-based material assessment, including non-destructive testing, welding QA, thickness measurement, painting thickness verification, and surface roughness.
Tissue handling/orientation and multi-modality imaging co-registration in connection with a sample container apparatus.
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