# US 12,217,837 B2 · Systems and methods for digitization of tissue slides based on associations among serial sections > Agents: this data is available as JSON from the patentgrep API and MCP server — see https://patentgrep.com/llms.txt - Number: US 12,217,837 B2 - Document ID: US12217837B2 - Status: Granted - Kind: B2 - Application number: 18/603,051 - Filed: 2024-03-12 - Published: 2024-11-14 - Granted: 2025-02-04 - Earliest claimed: 2023-05-09 - Assignees: Pramana, Inc. - Applicants: Pramana, Inc. - Inventors: Jithin Prems; Manish Shiralkar; Prasanth Perugupalli; Shilpa G. Krishna; Durgaprasad Dodle; Raghubansh Bahadur Gupta; Jaya Jain; Prateek Jain; Priyanka Golchha - Web page: https://patentgrep.com/patent/US12217837B2 ## CPC - G16H 10/40: for data related to laboratory analysis, e.g. patient specimen analysis - G16H: HEALTHCARE INFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR THE HANDLING OR PROCESSING OF MEDICAL OR HEALTHCARE DATA ## Abstract The present disclosure describes an exemplary system and method for the digitization of tissue slides based on associations among serial sections. The system includes at least a computing device, wherein the computing device includes a memory and processor communicatively connected to one another, and a scanner, configured to scan a slide and send a digitized image of the slide to the computing device. A method for the digitization of tissue slides based on associations among serial sections may include receiving a candidate tissue map associated with a candidate tissue section, receiving a reference tissue map associated with a reference tissue section, aligning the candidate tissue map to the reference tissue map, comparing the aligned candidate tissue map to the reference tissue map, and generating a regenerated candidate tissue map as a function of the reference tissue map. ## Claims ### Claim 1 A system for digitization of tissue slides based on associations among serial sections, wherein the system is comprised of: at least a computing device, wherein the computing device is comprised of: a memory, wherein the memory stores instructions; and a processor, communicatively connected to the memory, wherein the processor is configured to: retrieve, from the memory, a candidate tissue map associated with a candidate tissue section, wherein the candidate tissue map is digital and comprises a digitized and then scanned slide at a high magnification wherein the scanned slide is restricted to content within an identified enclosing bounding box; retrieve a reference tissue map associated with a reference tissue section; align the candidate tissue map to the reference tissue map; compare the aligned candidate tissue map to the reference tissue map; and generate a regenerated candidate tissue map as a function of the reference tissue map, wherein the regenerated candidate tissue map is digital and generating the regenerated candidate tissue map comprises instantiating a machine learning module which further comprises: receiving training data, wherein the training data correlates a plurality of reference tissue map data to a plurality of regenerated candidate tissue map data; training, iteratively, the machine learning module using the training data, wherein training the machine learning module includes retraining the machine learning module with feedback from previous iterations of the machine learning module; and generating the regenerated candidate tissue map using the trained machine learning module; and a scanner, configured to scan a slide and send a digitized image of the slide to the computing device. ### Claim 2 (depends on 1) The system of claim 1, wherein the memory further includes instructions configuring the processor to: identify a candidate serial section from at least one stain type, wherein the candidate serial section is associated with a case identification number and a block identification number; identify a reference serial section based on the cane identification number and the block identification number; generate the reference tissue map in response to scanning the reference serial section; and generate the candidate tissue map in response to scanning the candidate serial section. ### Claim 3 (depends on 1) The system of claim 1, wherein the candidate serial section is on a first slide and the reference serial section is on a second slide. ### Claim 4 (depends on 1) The system of claim 1 wherein the candidate serial section and the reference serial section are both on a first slide. ### Claim 5 (depends on 1) The system of claim 1, wherein the system is further comprised of at least a storage device. ### Claim 6 (depends on 1) The system of claim 1, wherein the system is further comprised of a scanned slides data repository. ### Claim 7 (depends on 1) The system of claim 1, wherein the system is further comprised of a regenerated slides data repository. ### Claim 8 (depends on 1) The system of claim 1, wherein the system is further comprised of both a scanned slides data repository and a regenerated slides data repository. ### Claim 9 (depends on 1) The system of claim 1, wherein the system instantiates a machine learning module. ### Claim 10 (depends on 1) The system of claim 1, wherein the system instantiates a neural network. ### Claim 11 A method for digitization of tissue slides based on associations among serial sections, wherein the method is comprised of: receiving, from a memory, a candidate tissue map associated with a candidate tissue section, wherein the candidate tissue map is digital and comprises a digitized and then scanned slide at a high magnification wherein the scanned slide is restricted to content within an identified enclosing bounding box; receiving a reference tissue map associated with a reference tissue section, the reference tissue section having a predetermined association with the candidate tissue section; aligning the candidate tissue map to the reference tissue map; comparing the aligned candidate tissue map to the reference tissue map; and generating a regenerated candidate tissue map as a function of the reference tissue map, wherein the regenerated candidate tissue map is digital and generating the regenerated candidate tissue map comprises instantiating a machine learning module which further comprises: receiving training data, wherein the training data correlates a plurality of reference tissue map data to a plurality of regenerated candidate tissue map data; training, iteratively, the machine learning module using the training data, wherein training the machine learning module includes retraining the machine learning module with feedback from previous iterations of the machine learning module; and generating the regenerated candidate tissue map using the trained machine learning module. ### Claim 12 (depends on 11) The method of claim 11, wherein the method further comprises: identifying a candidate serial section from at least one stain type, wherein the candidate serial section is associated with a case identification number and a block identification number; identifying a reference serial section based on the case identification number and the block identification number; generating the reference tissue map in response to scanning the reference serial section; and generating the candidate tissue map in response to scanning the candidate serial section. ### Claim 13 (depends on 11) The method of claim 11, wherein the candidate serial section is on a first slide and the reference serial section is on a second slide. ### Claim 14 (depends on 11) The method of claim 11, wherein the candidate serial section and the reference serial section are both on a first slide. ### Claim 15 (depends on 11) The method of claim 11, wherein the method further includes storage and retrieval of slides data from at least a storage device. ### Claim 16 (depends on 11) The method of claim 11, wherein the method further includes storage and retrieval of slides data from a scanned slides data repository. ### Claim 17 (depends on 11) The method of claim 11, wherein the method further includes storage and retrieval of slides data from a regenerated slides data repository. ### Claim 18 (depends on 11) The method of claim 11, wherein the method further includes storage and retrieval of slides data from both a scanned slides data repository and a regenerated slides data repository. ### Claim 19 (depends on 11) The method of claim 11, wherein the method instantiates a machine learning module. ### Claim 20 (depends on 11) The method of claim 11, wherein the method instantiates a neural network.