celldeath: a tool for detection of cell death in transmitted light microscopy images by deep learning-based visual recognition

Abstract

Cell death experiments are routinely done in many labs around the world, these experiments are the backbone of many assays for drug development. Cell death detection is usually performed in many ways, and requires time and reagents. However, cell death is preceded by slight morphological changes in cell shape and texture. In this paper, we trained a neural network to classify cells undergoing cell death. We found that the network was able to highly predict cell death after one hour of exposure to camptothecin. Moreover, this prediction largely outperforms human ability. Finally, we provide a simple python tool that can broadly be used to detect cell death.

Deep Learning-Assisted Nephrotoxicity Testing with Bioprinted Renal Spheroids

“…The full list of hyperparameters is provided in the Supplementary File. The concept of automated treatment effect readout was adapted from previous studies[ 11 , 12 ].…”

“…This again addresses important aspects of prospective screening applications, such as automation and high throughput. Here, we present a feasibility study for an automated toxicity readout using deep learning image classification based on bioprinted renal spheroids[ 11 , 12 ]. We trained a convolutional neural network (CNN) through supervised learning to predict the Ψ of a spheroid from its microscopic image.…”

LANCE: a Label-Free Live Apoptotic and Necrotic Cell Explorer Using Convolutional Neural Network Image Analysis

“…7,8 Prior work has used machine learning approaches to quantify cell viability by transmitted light microscopy. 6,9,10 This process is effective but complicated by the need for multiple programs each differing in complexity from unsupervised machine learning to convolutional neural networks in deep learning. Consequently, this process can differentiate between viable live cells and presumed dead cells, yet no effort is made to distinguish the different forms of cell death (e.g., apoptosis versus necrosis).…”

“…Similar tools have been developed using machine learning on transmitted light microscopy images with the purpose of determining cell death as it occurs solely by morphological changes. 6 The significance of this work lies in the idea of a single neural network being capable of cell death classification at high accuracies from digital images. Despite a high accuracy around 98.7%, this method is equally limited in that it cannot distinguish types of cell death.…”

“…Among machine learning strategies, the convolutional neural network (CNN) is especially good at image classification. 6 Inspired by the functionality of human neurons and synapses, CNN is composed of convolutional and pooling filters and connections between filters. Its multilayer structure recognizes both local and global features to mimic the judgment of a human observer for classification of images.…”