# US 11,908,130 B2 · Apparatuses and methods for digital pathology > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 11,908,130 B2 - Document ID: US11908130B2 - Status: Granted - Kind: B2 - Application number: 17/139,882 - Filed: 2020-12-31 - Published: 2021-07-08 - Granted: 2024-02-20 - Earliest claimed: 2020-01-03 - Assignees: Protein Metrics Inc. - Applicants: Protein Metrics, LLC - Inventors: Michael J. Natan; Marshall Bern; Eric Carlson - Web page: https://patentgrep.com/patent/US11908130B2 ## CPC - G06T 7/0012: {Biomedical image inspection} - G01N 33/4833: {of solid biological material, e.g. tissue samples, cell cultures (tissue in vivo A61B5/00; cell suspensions G01N33/48735)} - G06N 3/0464: Convolutional networks [CNN, ConvNet] - G06N 3/09: Supervised learning - G06N 20/00: Machine learning - G06T 7/11: Region-based segmentation - G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis - G16H 30/40: for processing medical images, e.g. editing - G16H 50/20: for computer-aided diagnosis, e.g. based on medical expert systems - G16H 70/60: relating to pathologies - G06N 3/045: Combinations of networks - G06T 2207/10056: Microscopic image - G06T 2207/30024: Cell structures in vitro; Tissue sections in vitro - G06T 2207/30204: Marker - G06T: IMAGE DATA PROCESSING OR GENERATION, IN GENERAL - G01N: INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES (measuring or testing apparatus or processes other than immunoassay, involving enzymes or microorganisms C12M, C12Q) - G06N: COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract Digital pathology is a promising alternative to manual slide analysis, but improvements in imaging and analysis methods are needed to provide an analysis data set of images that can easily be handled, stored, and importantly, delivered from one data location to another. Methods and software are described herein to improve image collection and analysis. ## Claims ### Claim 1 A method for generating image data for a set of stained slides that comprises a tissue sample, the method comprising: selecting a plurality of sub-regions from one of the set of stained slides; identifying, for each imaging channel of a first plurality of imaging channels, either a first number of variations of contrast thresholds, a first number of variations of thresholds of marker positivity, or a first number of variations of contrast thresholds and a first number of variations of thresholds of marker positivity, wherein each imaging channel is associated with a corresponding biomarker or plurality of biomarkers; generating a plurality of masks for each of the plurality of sub-regions corresponding to each combination of the variations of contrast thresholds and variations of thresholds of marker positivity for each imaging channel of the first plurality of imaging channels, wherein each mask is segmented to identify cells; ranking the plurality of masks based on an ability of each of the plurality of masks to accurately identify cells that are positive for the corresponding biomarker or plurality of biomarkers of each imaging channel of the first plurality of imaging channels; selecting a set of final mask parameters from the plurality of masks based on the rankings; and generating a set of slide images for all slides of the set of stained slides using the set of final mask parameters. ### Claim 2 (depends on 1) The method of claim 1, wherein selecting the plurality of sub-regions from one of the set of stained slides comprises selecting the sub-regions so that at least one of the sub-regions includes a structure that is substantially free of cells. ### Claim 3 (depends on 1) The method of claim 1, wherein identifying further comprises identifying a number of variations of image acquisition parameters for each imaging channel of the first plurality of imaging channels. ### Claim 4 (depends on 1) The method of claim 1, further comprising selecting a plurality of data compression values. ### Claim 5 (depends on 1) The method of claim 1, wherein the ranking is performed by a trained network. ### Claim 6 (depends on 1) The method of claim 1, further comprising iterating the steps of identifying and generating for different imaging channels. ### Claim 7 (depends on 1) The method of claim 1, further comprising determining, from the set of stained slides, statistics on labeled cells for a second plurality of imaging channels that is larger than the first plurality of imaging channels. ### Claim 8 (depends on 1) The method of claim 1, wherein selecting the plurality of sub-regions from one of the set of stained slides comprises selecting a different size for each of the sub-regions. ### Claim 9 (depends on 1) The method of claim 1, wherein each mask is segmented to identify cells using the variations of contrast thresholds and the variations of thresholds of marker positivity. ### Claim 10 A method for generating image data for a set of stained slides that comprises a tissue sample, the method comprising: selecting a plurality of sub-regions from one of the set of stained slides, wherein at least one of the sub-regions includes a structure that is substantially free of cells; identifying, for each imaging channel of a first plurality of imaging channels: a first number of variations of contrast thresholds, a first number of variations of thresholds of marker positivity, or a first number of variations of contrast thresholds and a first number of variations of thresholds of marker positivity, wherein each imaging channel is associated with a corresponding biomarker or plurality of biomarkers; generating a plurality of masks comprising a mask for each of the plurality of sub-regions corresponding to each combination of the variations of contrast thresholds and the variations of thresholds of marker positivity for each imaging channel of the first plurality of imaging channels, wherein each mask is segmented to identify cells using the variations of contrast thresholds and the variations of thresholds of marker positivity; ranking the plurality of masks based on an ability of each of the plurality of masks to accurately identify cells that are positive for the corresponding biomarker or plurality of biomarkers of each imaging channel of the first plurality of imaging channels; selecting a set of final mask parameters from the plurality of masks based on the rankings; and generating a set of slide images for all slides of the set of stained slides using the set of final mask parameters. ### Claim 11 A system for generating image data for a set of stained slides that comprises a tissue sample, the system comprising: one or more processors; memory coupled to the one or more processors, the memory configured to store computer-program instructions, that, when executed by the one or more processors, perform a computer-implemented method comprising: selecting a plurality of sub-regions from one of the set of stained slides; identifying, for each imaging channel of a first plurality of imaging channels, either a first number of variations of contrast thresholds, a first number of variations of thresholds of marker positivity, or a first number of variations of contrast thresholds and a first number of variations of thresholds of marker positivity, wherein each imaging channel is associated with a corresponding biomarker or plurality of biomarkers; generating a plurality of masks for each of the plurality of sub-regions corresponding to each combination of the variations of contrast thresholds and variations of thresholds of marker positivity for each imaging channel of the first plurality of imaging channels, wherein each mask is segmented to identify cells; ranking the plurality of masks based on an ability of each of the plurality of masks to accurately identify cells that are positive for the corresponding biomarker or plurality of biomarkers of each imaging channel of the first plurality of imaging channels; selecting a set of final mask parameters from the plurality of masks based on the rankings; and generating a set of slide images for all slides of the set of stained slides using the set of final mask parameters. ### Claim 12 (depends on 11) The system of claim 11, wherein selecting the plurality of sub-regions from one of the set of stained slides comprises selecting each of the sub-regions so that each sub-region includes a structure that is substantially free of cells. ### Claim 13 (depends on 11) The system of claim 11, wherein identifying further comprises identifying a number of variations of image acquisition parameters for each imaging channel of the first plurality of imaging channels. ### Claim 14 (depends on 11) The system of claim 11, further comprising selecting a plurality of data compression values. ### Claim 15 (depends on 11) The system of claim 11, wherein the ranking is performed by a trained network. ### Claim 16 (depends on 11) The system of claim 11, further comprising iterating the steps of identifying and generating for different imaging channels. ### Claim 17 (depends on 11) The system of claim 11, further comprising determining, from the set of stained slides, statistics on labeled cells for a second plurality of imaging channels that is larger than the first plurality of imaging channels. ### Claim 18 (depends on 11) The system of claim 11, wherein selecting the plurality of sub-regions from one of the set of stained slides comprises selecting a different size for each of the sub-regions. ### Claim 19 (depends on 11) The system of claim 11, wherein each mask is segmented to identify cells using the variations of contrast thresholds and the variations of thresholds of marker positivity.