Document Type
Article
Publication Date
February 2025
Patent Number
12229959
CPC
G06T 7/0012 , G06T 2207/10056 , G06T 2207/10064 , G06V 10/82
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.
Application Number
17/971295
Recommended Citation
Dave, Palak Pankajbhai; Goldgof, Dmitry; Hall, Lawrence O.; and Mouton, Peter R., "Systems and methods for determining cell number count in automated stereology z-stack images" (2025). USF Patents. 1487.
https://digitalcommons.usf.edu/usf_patents/1487
Assignees
UNIVERSITY OF SOUTH FLORIDA
Filing Date
10/21/2022
