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    AUTOMATED STEREOLOGY FOR DETERMINING TISSUE CHARACTERISTICS
    3.
    发明申请
    AUTOMATED STEREOLOGY FOR DETERMINING TISSUE CHARACTERISTICS 有权

    公开(公告)号：US20220058369A1

    公开(公告)日：2022-02-24

    申请号：US17397891

    申请日：2021-08-09

    Applicant: UNIVERSITY OF SOUTH FLORIDA ,  STEREOLOGY RESOURCE CENTER, INC.

    Inventor： Saeed S. Alahmari ,  Dmitry Goldgof ,  Lawrence O. Hall ,  Peter R. Mouton

    IPC: G06K9/00 ,  G06K9/62 ,  G06K9/34 ,  G06K9/40 ,  G02B21/36 ,  G02B21/26

    Abstract: Systems and methods for automated stereology are provided. In some embodiments, an active deep learning approach may be utilized to allow for a faster and more efficient training of a deep learning model for stereology analysis. In other embodiments, existing deep learning models for stereology analysis may be re-tuned to develop greater accuracy for a given data set of interest, either with or without an active deep learning approach. A method can include: capturing a data set including a stack of images of a three-dimensional (3D) object; determining whether an existing deep learning model is appropriate for use on the stack of images (or for re-tuning); performing pre-processing on the data set; performing a training of a deep learning model; applying the deep learning model to obtain a confidence score for each label of the data set; reviewing, by a user, at least some labels in the active set to verify whether the label displays sufficient agreement with an expected result, and moving only those that display sufficient agreement to a training set; and performing a stereology analysis using the trained deep learning model.

    AUTOMATED STEREOLOGY FOR DETERMINING TISSUE CHARACTERISTICS
    10.
    发明申请
    AUTOMATED STEREOLOGY FOR DETERMINING TISSUE CHARACTERISTICS 有权

    公开(公告)号：US20230127698A1

    公开(公告)日：2023-04-27

    申请号：US17971295

    申请日：2022-10-21

    Applicant: UNIVERSITY OF SOUTH FLORIDA ,  STEREOLOGY RESOURCE CENTER, INC.

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

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

    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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