Non-destructive fibroblast NIH-3T3 spheroid classification using convolutional neural network

Abstract

Cellular spheroid is a complex aggregate of cells, and imaging is an important step in understanding how cell behavior operates and give references about possibilities according to the cells in the spheroid formation. Identifying structures in these images is manual and time-consuming and have a high rate of variability inter experts. An automated identification can solve these problems. The aim of this work is to present a study of Convolutional Neural Network (CNN) applied to live cells identification in NIH-3T3 spheroid. Four different CNN architectures are exploited in this paper: AlexNet, ResNet18, GoogLeNet, and VGG 16 with batch normalization. Many experiments were performed to get the best architecture involving data augmentation, hyperparameter tuning, and transfer learning using ImageNet. The experiments identify up to five different structures in a spheroid image, where the AlexNet achieved the best performance considering the F1-score as the evaluation metric. The use of CNN for this kind of identification opens the possibility of following the spheroid's behaviour when cultured in more complex images.