Abstract: A device, system and method for providing ancillary objects from a cache and/or for providing categorized provider objects is provided. One or more servers receive a flight object representing a flight and search an ancillary object cache for predetermined ancillary objects associated with previous flights similar to the flight. When one or more of the predetermined ancillary objects, associated with at least one previous flight similar to the flight, are found at the ancillary object cache, a requesting device is provided with a response corresponding to the flight object and the one or more of the predetermined ancillary objects associated with the at least one previous flight similar to the flight. The flight objects and the ancillary objects may be assembled into provider objects which are provided to the requesting device. The provider objects may be categorized based on provider object categorization criteria associated with the requesting device.

Abstract: A device, system and method for providing ancillary objects from a cache and/or for providing categorized provider objects is provided. One or more servers receive a flight object representing a flight and search an ancillary object cache for predetermined ancillary objects associated with previous flights similar to the flight. When one or more of the predetermined ancillary objects, associated with at least one previous flight similar to the flight, are found at the ancillary object cache, a requesting device is provided with a response corresponding to the flight object and the one or more of the predetermined ancillary objects associated with the at least one previous flight similar to the flight. The flight objects and the ancillary objects may be assembled into provider objects which are provided to the requesting device. The provider objects may be categorized based on provider object categorization criteria associated with the requesting device.

Abstract: The disclosed subject matter relates to an automated determination of cell-by-cell segmentation quality of a tissue specimen sample. A training set of cells is examined by an expert to determine which cells that include “good” segmentation and which cells include “poor” segmentation. A training model is build based on the image data of the cells in the training set. Image data from cells in a test specimen is obtained and that image data is compared to the training model to determine on a cell-by-cell basis which cells in the test specimen include “good” segmentation and which cells include “poor” segmentation. The accumulated data on the cells analyzed in the test specimen can be utilized to determine an overall segmentation quality score for the area of the test specimen in which the individual cells are located in the test specimen.

Abstract: The disclosed embodiments are directed to a method for accurately counting and characterizing multiple cell phenotypes and sub-phenotypes within cell populations simultaneously by exploiting biomarker co-expression levels within cells of different phenotypes in the same tissue sample. The disclosed embodiments are also directed to a simple intuitive interface enabling medical staff (e.g., pathologists, biologists) to annotate and evaluate different cell phenotypes used in the algorithm and the presented through the interface.

Abstract: The disclosed subject matter relates to an automated determination the stain quality and segmentation quality of a tissue sample. By way of example, separate image data is acquired of an unstained form of a biological specimen, the biological specimen stained with a nuclei marker and the biological specimen stained with a segmentation marker. A correlation map (Cr) from the separate image data and a ridgeness map (Pr) from the image data of the biological specimen stained with a segmentation marker are each determined. A staining quality score and segmentation quality score are then determined from the correlation map (Cr) and the ridgeness map (Pr).

Abstract: The disclosed subject matter relates to an automated determination of cell-by-cell segmentation quality of a tissue specimen sample. A training set of cells is examined by an expert to determine which cells that include “good” segmentation and which cells include “poor” segmentation. A training model is build based on the image data of the cells in the training set. Image data from cells in a test specimen is obtained and that image data is compared to the training model to determine on a cell-by-cell basis which cells in the test specimen include “good” segmentation and which cells include “poor” segmentation. The accumulated data on the cells analyzed in the test specimen can be utilized to determine an overall segmentation quality score for the area of the test specimen in which the individual cells are located in the test specimen.

Abstract: The disclosed embodiments are directed to a method for accurately counting and characterizing multiple cell phenotypes and sub-phenotypes within cell populations simultaneously by exploiting biomarker co-expression levels within cells of different phenotypes in the same tissue sample. The disclosed embodiments are also directed to a simple intuitive interface enabling medical staff (e.g., pathologists, biologists) to annotate and evaluate different cell phenotypes used in the algorithm and the presented through the interface.

Abstract: The disclosed subject matter relates to an automated determination the stain quality and segmentation quality of a tissue sample. By way of example, separate image data is acquired of an unstained form of a biological specimen, the biological specimen stained with a nuclei marker and the biological specimen stained with a segmentation marker. A correlation map (Cr) from the separate image data and a ridgeness map (Pr) from the image data of the biological specimen stained with a segmentation marker are each determined. A staining quality score and segmentation quality score are then determined from the correlation map (Cr) and the ridgeness map (Pr).