Establishing a reference focal plane using convolutional neural networks and beads for brightfield imaging.

ABSTRACT: Repeatability of measurements from image analytics is difficult, due to the heterogeneity and complexity of cell samples, exact microscope stage positioning, and slide thickness. We present a method to define and use a reference focal plane that provides repeatable measurements with very high accuracy, by relying on control beads as reference material and a convolutional neural network focused on the control bead images. Previously we defined a reference effective focal plane (REFP) based on the image gradient of bead edges and three specific bead image features. This paper both generalizes and improves on this previous work. First, we refine the definition of the REFP by fitting a cubic spline to describe the relationship between the distance from a bead's center and pixel intensity and b

SUBMITTER: Chalfoun J 

PROVIDER: S-EPMC10987482 | biostudies-literature | 2024 Apr

REPOSITORIES: biostudies-literature