Mitotic index determination on live cells from label-free acquired quantitative phase images using a supervised autoencoder

Philippe Pognonec (1) , Axel Gustovic , Zied Djabari , Thierry Pourcher (1) , Michel Barlaud (2)

1 UCA Faculté Médecine - Université Côte d'Azur - Faculté de Médecine
2 Laboratoire I3S - SIS - Signal, Images et Systèmes

This interdisciplinary work focuses on the interest of a new auto-encoder for supervised classification of live cell populations growing in a thermostated imaging station and acquired by a Quantitative Phase Imaging (QPI) camera. This type of camera produces interferograms that have to be processed to extract features derived from quantitative linear retardance and birefringence measurements. QPI is performed on living populations without any manipulation or treatment of the cells. We use the efficient new autoencoder classification method instead of the classical Douglas-Rachford method. Using this new supervised autoencoder, we show that the accuracy of the classification of the cells present in the mitotic phase of the cell cycle is very high using QPI features. This is a very important finding since we demonstrate that it is now possible to very precisely follow cell growth in a non-invasive manner, without any bias. No dye or any kind of markers are necessary for this live monitoring. Any studies requiring analysis of cell growth or cellular response to any treatment could benefit from this new approach by simply monitoring the proportion of cells entering mitosis in the studied cell population.

Mots clés: Mitotic index; non-invasive cell monitoring; Quantitative Phase Imaging (QPI); Supervised autoencoder

Domaines: Machine Learning [stat.ML]; Sciences du Vivant [q-bio]

Philippe Pognonec, Axel Gustovic, Zied Djabari, Thierry Pourcher, Michel Barlaud. Mitotic index determination on live cells from label-free acquired quantitative phase images using a supervised autoencoder. IEEE/ACM Transactions on Computational Biology and Bioinformatics, 2021. ⟨hal-03364377⟩