US 12,217,837 B2
Systems and methods for digitization of tissue slides based on associations among serial sections
Jithin Prems, Dooravani Nagar (IN); Manish Shiralkar, Pune (IN); Prasanth Perugupalli, Cary, NC (US); Shilpa G. Krishna, Kerala (IN); Durgaprasad Dodle, Telangana (IN); Raghubansh Bahadur Gupta, Bangalore (IN); Jaya Jain, Shahpura (IN); Prateek Jain, Karnataka (IN); and Priyanka Golchha, Rajasthan (IN)
Assigned to Pramana, Inc., Cambridge, MA (US)
Filed by Pramana, Inc., Cambridge, MA (US)
Filed on Mar. 12, 2024, as Appl. No. 18/603,051.
Claims priority of provisional application 63/465,032, filed on May 9, 2023.
Prior Publication US 2024/0379197 A1, Nov. 14, 2024
Int. Cl. G16H 10/40 (2018.01)
CPC G16H 10/40 (2018.01) 20 Claims
OG exemplary drawing
 
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.