Applying Deep Learning to Melanocyte Counting on Fluorescent TRP1 Labelled Images of In Vitro Skin Model

Authors

  • Tristan Lazard Mines Paris, PSL University, Centre for Mathematical Morphology
  • Samy Blusseau Mines Paris, PSL University, Centre for Mathematical Morphology https://orcid.org/0000-0003-0294-8172
  • Santiago Velasco-Forero Mines Paris, PSL University, Centre for Mathematical Morphology
  • Étienne Decencière Mines Paris, PSL University, Centre for Mathematical Morphology
  • Virginie Flouret L'Oréal Research and Innovation
  • Catherine Cohen L'Oréal Research and Innovation
  • Thérèse Baldeweck L'Oréal Research and Innovation

DOI:

https://doi.org/10.5566/ias.2640

Keywords:

cell counting, convolutional neural networks (CNN), deep learning, fluorescence microscopy, histology, immunohistochemistry (IHC), reconstructed skin

Abstract

Cell counting is an important step in many biological experiments. It can be challenging, due to the large variability in contrast and shape of the cells, especially when their density is so high that the cells are closely packed together. Automation is needed to increase the speed and quality of the detection. In this study, a cell counting method is developed for images of melanocytes obtained after fluorescent labelling with TRP1 (Tyrosinase-related protein 1) of 3D reconstructed skin samples. Following previous approaches, a strategy based on predicting the local cell density, by means of a convolutional neural network (a U-Net), was adopted. The method showed great efficiency on a test set of 76 images, with an assessed counting error close to 10% on average, which is a commonly accepted target in cytology and histology. For comparison purposes, we have made our dataset publicly available.

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Published

2022-11-30

How to Cite

Lazard, T., Blusseau, S., Velasco-Forero, S., Decencière, Étienne, Flouret, V., Cohen, C., & Baldeweck, T. (2022). Applying Deep Learning to Melanocyte Counting on Fluorescent TRP1 Labelled Images of In Vitro Skin Model. Image Analysis and Stereology, 41(3), 171–180. https://doi.org/10.5566/ias.2640

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Original Research Paper

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