Applying deep learning to melanocyte counting on fluorescent TRP1 labelled images of in vitro skin model

Abstract

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

Determination of droplet size from wide-angle light scattering image data using convolutional neural networks

“…Such CNN-based approaches have been applied to a variety of different tasks, e.g. the efficient labeling of image data, see [18], or image classification and regression tasks, see [9,10,25]. While first attempts to apply machine learning algorithms for the evaluation of scattering data from nanoparticles and heterogenous materials were successful [19,23], a similar approach for the evaluation of scattering from single droplets has not been applied so far.…”