US 12,380,578 B2
Apparatus and a method for on-demand registration of whole slide images
Jithin Prems, Dooravani Nagar (IN); Manish Shiralkar, Pune (IN); Prasanth Perugupalli, Cary, NC (US); Jaya Jain, Shahpura (IN); Raghubansh Bahadur Gupta, Bangalore (IN); Tasin Ahmed, Scarborough (CA); Falgun Meshram, Toronto (CA); Seth Marquardt, Berkeley, CA (US); and Bharathwaj Raghunathan, Oakville (CA)
Assigned to Pramana, Inc., Cambridge, MA (US)
Filed by Pramana, Inc., Cambridge, MA (US)
Filed on Jun. 14, 2024, as Appl. No. 18/744,575.
Claims priority of provisional application 63/508,785, filed on Jun. 16, 2023.
Prior Publication US 2024/0420352 A1, Dec. 19, 2024
Int. Cl. G06T 7/33 (2017.01); G06T 7/37 (2017.01)
CPC G06T 7/337 (2017.01) [G06T 7/37 (2017.01); G06T 2207/10056 (2013.01); G06T 2207/20016 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/30004 (2013.01)] 18 Claims
OG exemplary drawing
 
1. An apparatus for on-demand registration of whole slide images (WSIs), the apparatus comprises: at least a processor; and a memory communicatively connected to the at least a processor, wherein the memory contains instructions configuring the at least a processor to: receive, at least two digital images taken at a first magnification level, wherein each digital image of the at least two images comprises a WSI in a pyramid structure wherein the base layer of the pyramid structure comprises a highest resolution image and each subsequent layer of the pyramid structure includes lower resolution images and wherein the at least two digital images comprises: a first digital image of a reference slide; and a second digital image of a candidate slide; identify a first region of interest (ROI) on the first digital image at a target magnification level; training a computer vision model using training data, wherein the training data comprises digitized histological slide images at a plurality of magnification levels as input correlated to a plurality of ROIs as output; and identifying the first ROI as a function of the first digital image using the trained computer vision model;
register at least a portion of the second digital image to at least a portion of the first digital slide image at the first magnification level to derive a transformation matrix; apply the transformation matrix comprising transformation shearing parameters to the first ROI on the first digital image to identify a second ROI on the second digital image at the first magnification level; map the second ROI on the second digital image to a corresponding second ROI on the second digital image at the target magnification level;
and register the corresponding second ROI on the second digital image to the first ROI on the first digital image at the target magnification level.