US 12,229,959 B2
Systems and methods for determining cell number count in automated stereology z-stack images
Palak Pankajbhai Dave, Wesley Chapel, FL (US); Dmitry Goldgof, Lutz, FL (US); Lawrence O. Hall, Tampa, FL (US); and Peter R. Mouton, Gulfport, FL (US)
Assigned to UNIVERSITY OF SOUTH FLORIDA, Tampa, FL (US); and STEREOLOGY RESOURCE CENTER, INC., St. Petersburg, FL (US)
Filed by UNIVERSITY OF SOUTH FLORIDA, Tampa, FL (US); and STEREOLOGY RESOURCE CENTER, INC., Saint Petersburg, FL (US)
Filed on Oct. 21, 2022, as Appl. No. 17/971,295.
Application 17/971,295 is a continuation of application No. 17/308,592, filed on May 5, 2021, granted, now 11,803,968.
Application 17/308,592 is a continuation of application No. 16/345,392, granted, now 11,004,199, previously published as PCT/US2017/061090, filed on Nov. 10, 2017.
Claims priority of provisional application 62/420,771, filed on Nov. 11, 2016.
Claims priority of provisional application 63/263,198, filed on Oct. 28, 2021.
Claims priority of provisional application 63/357,946, filed on Jul. 1, 2022.
Prior Publication US 2023/0127698 A1, Apr. 27, 2023
Int. Cl. G06T 7/00 (2017.01); G06N 3/08 (2023.01); G06N 20/00 (2019.01); G06T 5/20 (2006.01); G06T 5/70 (2024.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06T 17/20 (2006.01); G06V 20/69 (2022.01)
CPC G06T 7/0014 (2013.01) [G06N 3/08 (2013.01); G06N 20/00 (2019.01); G06T 5/20 (2013.01); G06T 5/70 (2024.01); G06T 7/11 (2017.01); G06T 7/174 (2017.01); G06T 7/97 (2017.01); G06T 17/205 (2013.01); G06V 20/695 (2022.01); G06V 20/698 (2022.01); G06T 2207/20081 (2013.01); G06T 2207/20084 (2013.01); G06T 2207/20152 (2013.01)] 25 Claims
OG exemplary drawing
 
1. A method for performing computerized stereology, comprising:
obtaining a plurality of z-stack runtime images including one or more cells;
generating a plurality of z-stack grayscale images by converting the plurality of z-stack runtime images into grayscale;
applying the plurality of z-stack grayscale images to a trained deep-learning model, each z-stack grayscale image of the plurality of z-stack grayscale images corresponding to an input channel of the trained deep-learning model;
obtaining a plurality of outputs corresponding to the plurality of z-stack grayscale images from the trained deep-learning model, the plurality of outputs comprising information indicative of a plane of best focus for each cell of the one or more cells of the plurality of z-stack runtime images, wherein a first output N of the plurality of outputs is bidirectionally correlated with previous (N−1) and subsequent (N+1) outputs of the plurality of outputs; and
counting the one or more second cells in the plurality of outputs.