Pipette Hunter 3D: Fluorescent Micropipette Detection

D. Hirling (), K. Koos (), J. Molnár () and P. Horvath ()
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D. Hirling: Biological Research Centre of Szeged
K. Koos: Biological Research Centre of Szeged
J. Molnár: Biological Research Centre of Szeged
P. Horvath: Biological Research Centre of Szeged

A chapter in Trends in Biomathematics: Modeling Cells, Flows, Epidemics, and the Environment, 2020, pp 111-125 from Springer

Abstract: Abstract Segmentation of objects with known geometries in an image is a fundamental research problem. In this paper we show a three-dimensional energy minimization model to detect the tip of glass micropipettes in fluorescence microscopy image stacks. The described model fits a truncated cone to the bright intensities in the image. The model is minimized using gradient descent. The number of parameters is fairly low to achieve fast evolution and noise insensitivity. The algorithm is tested on fluorescence microscopy image stacks. The error of the tip detection is only a few micrometers. Automatic pipette tip detection is a step forward to automate the patch clamp process, used for recording the electrophysiological properties of living neurons or myocytes. The described method can easily be extended to other applications or find multiple objects with a connection, e.g., lines for road intersection detection.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-46306-9_8

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DOI: 10.1007/978-3-030-46306-9_8

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