公开(公告)号：US20230127698A1

公开(公告)日：2023-04-27

申请号：US17971295

申请日：2022-10-21

申请人： UNIVERSITY OF SOUTH FLORIDA ,  STEREOLOGY RESOURCE CENTER, INC.

发明人： Palak Pankajbhai Dave ,  Dmitry Goldgof ,  Lawrence O. Hall ,  Peter R. Mouton

IPC分类号： G06T7/00 ,  G06T7/11 ,  G06T7/174 ,  G06N20/00 ,  G06N3/08 ,  G06T5/00 ,  G06T5/20 ,  G06T17/20 ,  G06V20/69

摘要： 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.