US 12,412,316 B2
Systems and methods to process electronic images to adjust attributes of the electronic images
Navid Alemi, Conventry (GB); Christopher Kanan, Pittsford, NY (US); and Leo Grady, Darien, CT (US)
Assigned to Paige.AI, Inc., New York, NY (US)
Filed by PAIGE.AI, Inc., New York, NY (US)
Filed on Jul. 21, 2022, as Appl. No. 17/814,072.
Application 17/814,072 is a continuation of application No. 17/457,962, filed on Dec. 7, 2021, granted, now 11,455,724.
Application 17/457,962 is a continuation of application No. 17/643,036, filed on Dec. 7, 2021, granted, now 11,455,753.
Claims priority of provisional application 63/187,685, filed on May 12, 2021.
Prior Publication US 2022/0366619 A1, Nov. 17, 2022
This patent is subject to a terminal disclaimer.
Int. Cl. G06T 7/00 (2017.01); G06N 20/00 (2019.01); G06T 3/40 (2024.01); G06T 5/00 (2024.01); G06T 7/11 (2017.01); G06T 7/90 (2017.01); G06T 11/00 (2006.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G16H 70/60 (2018.01)
CPC G06T 7/0012 (2013.01) [G06N 20/00 (2019.01); G06T 3/40 (2013.01); G06T 5/00 (2013.01); G06T 7/11 (2017.01); G06T 7/90 (2017.01); G06T 11/001 (2013.01); G16H 10/40 (2018.01); G16H 30/40 (2018.01); G16H 70/60 (2018.01); G06T 2200/24 (2013.01); G06T 2207/10024 (2013.01); G06T 2207/10056 (2013.01); G06T 2207/20081 (2013.01); G06T 2207/20092 (2013.01); G06T 2207/30024 (2013.01); G06T 2210/41 (2013.01)] 20 Claims
OG exemplary drawing
 
1. A system for processing electronic images to adjust stains, the system comprising:
a data store storing a plurality of machine-learned transformations associated with a plurality of stain types;
a processor; and
a memory coupled to the processor and storing instructions that, when executed by the processor, cause the system to perform operations comprising:
receiving, as input, an image of a slide-mounted tissue sample subjected to overstaining or understaining with a stain during preparation, wherein the image is comprised of a plurality of pixels in a first color space and includes the stain;
determining, from the plurality of machine-learned transformations stored in the data store, a machine-learned transformation associated with a stain type of the stain;
applying the machine-learned transformation to the plurality of pixels to convert the plurality of pixels from the first color space to a second color space, the second color space being specific to the stain type;
correcting for the overstaining or the understaining by adjusting an amount of the stain in the second color space to generate a stain-adjusted plurality of pixels;
converting the stain-adjusted plurality of pixels from the second color space to the first color space using an inverse of the machine-learned transformation; and
outputting a stain-adjusted image including the stain-adjusted plurality of pixels in the first color space.