- Patent Title: Methods, systems, and computer readable media for using synthetically trained deep neural networks for automated tracking of particles in diverse video microscopy data sets
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Application No.: US16379466Application Date: 2019-04-09
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Publication No.: US10664978B2Publication Date: 2020-05-26
- Inventor: Jay Mack Newby , M. Gregory Forest , Samuel Kit Bunn Lai
- Applicant: The University of North Carolina at Chapel Hill
- Applicant Address: US NC Chapel Hill
- Assignee: The University of North Carolina at Chapel Hill
- Current Assignee: The University of North Carolina at Chapel Hill
- Current Assignee Address: US NC Chapel Hill
- Agency: Jenkins, Wilson, Taylor & Hunt, P.A.
- Main IPC: G06T7/20
- IPC: G06T7/20 ; G06N3/08 ; G06N20/00 ; G06F17/50 ; G02B21/36

Abstract:
A method for using a synthetically trained neural network for tracking particle movement in video microscopy data includes receiving, as input, video microscopy data representing images of particles that move between video frames. The method includes using a synthetically trained neural network to track movement of the particles between the video frames, wherein the synthetically trained neural network comprises a neural network that is trained on a plurality of different simulated video microscopy data sets. The method further includes outputting, by the synthetically trained neural network, an indication of movement of the particles between the video frames.
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