The patent badge is an abbreviated version of the USPTO patent document. The patent badge does contain a link to the full patent document.

The patent badge is an abbreviated version of the USPTO patent document. The patent badge covers the following: Patent number, Date patent was issued, Date patent was filed, Title of the patent, Applicant, Inventor, Assignee, Attorney firm, Primary examiner, Assistant examiner, CPCs, and Abstract. The patent badge does contain a link to the full patent document (in Adobe Acrobat format, aka pdf). To download or print any patent click here.

Date of Patent:
Feb. 18, 2025

Filed:

Oct. 21, 2022
Applicants:

University of South Florida, Tampa, FL (US);

Stereology Resource Center, Inc., Saint Petersburg, FL (US);

Inventors:

Palak Pankajbhai Dave, Wesley Chapel, FL (US);

Dmitry Goldgof, Lutz, FL (US);

Lawrence O. Hall, Tampa, FL (US);

Peter R. Mouton, Gulfport, FL (US);

Assignees:

UNIVERSITY OF SOUTH FLORIDA, Tampa, FL (US);

STEREOLOGY RESOURCE CENTER, INC., St. Petersburg, FL (US);

Attorney:
Int. Cl.
CPC ...
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);
U.S. Cl.
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);
Abstract

Systems and methods for automated stereology using deep learning are disclosed. The systems include an update in the form of a semi-automatic approach for ground truth preparation in 3D stacks of microscopy images (disector stacks) for generating more training data. The systems also present an exemplary disector-based MIMO framework where all the planes of a 3D disector stack are analyzed as opposed to a single focus-stacked image (EDF image) per stack. The MIMO approach avoids the costly computations of 3D deep learning-based methods by using the 3D context of cells in disector stacks; and prevents stereological bias in the previous EDF-based method due to counting profiles rather than cells and under-counting overlap-ping/occluded cells. Taken together, these improvements support the view that AI-based automatic deep learning methods can accelerate the efficiency of unbiased stereology cell counts without a loss of accuracy or precision as compared to conventional manual stereology.


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