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

Greca, Alejandro La; Pérez, Nelba; Castañeda, Sheila; Milone, Paula Melania; Scarafía, María Agustina; Möbbs, Alan Miqueas; Waisman, Ariel; Moro, Lucía; Sevlever, Gustavo; Luzzani, Carlos; Miriuka, Santiago Gabriel

doi:10.1371/journal.pone.0253666

Cited by 16 publications

(13 citation statements)

References 37 publications

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“…Pre-trained CNNs have already provided excellent results to detect cell death or differentiation for adherent cells on a 2D surface 27,29,35 . Interestingly, such CNNs did not provide a better accuracy than ours (91.2%) while requiring larger computing times (See Table 1).…”

Section: Discussionmentioning

confidence: 99%

Machine learning-based detection of label-free cancer stem-like cell fate

Chambost

Berabez

Cochet‐Escartin

et al. 2022

Preprint

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Most imaging methods rely on labelling biological samples in order to provide specific and easily detectable features. However, label-free imaging is a non-invasive and non-toxic alternative that requires accurate image analysis algorithm based on cell morphology. Such analysis has to deal with a high image variability while fewer features are extractable, so far, fast analysis of label-free brightfield microscopy (LFBM) images remains a challenging task. With the development of microfabricated devices during the last decades, high throughput image generation makes it possible to use machine learning-based algorithms in order to analyse LFBM images. Fast algorithms are also crucially needed to analyze high throughput experiments. In this paper, we provide a data-driven study in order to assess the complexity of LFBM time-lapses monitoring isolated cancer stem-like cells (CSCs) fate in non-adherent conditions. We combined for the first time individual cell fate and cell state temporality analysis in a unique algorithm. Several image analysis algorithms of increasing processing capacities were tested: a classical computer vision algorithm (CCVA), a shallow learning-based algorithm (SLBA) and a deep learning-based algorithm (DLBA). We show that our optimized DLBA has by far the best accuracy compared to CCVA and SLBA, is at least as accurate as other state-of-the-art DLBAs while being faster. With this study, we demonstrate that optimizing our DLBA accordingly to the image analysis problem can overall provide better results than pretrained models. Such a fast and accurate DLBA is therefore compatible with the generation of high throughput data and opens the route for on-the-fly analysis of CSC fate from LFBM time-lapses.

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Section: Discussionmentioning

confidence: 99%

Machine learning-based detection of label-free cancer stem-like cell fate

Chambost

Berabez

Cochet‐Escartin

et al. 2022

Preprint

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“…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. , Prior work has used machine learning approaches to quantify cell viability by transmitted light microscopy. ,, 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).…”

Section: Introductionmentioning

confidence: 99%

“…The recent advancement in machine learning opens avenues for image classification and automatic recognition in high throughput. Among machine learning strategies, the convolutional neural network (CNN) is especially good at image classification . Inspired by the functionality of human neurons and synapses, CNN is composed of convolutional and pooling filters and connections between filters.…”

Section: Introductionmentioning

confidence: 99%

“…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 . 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.…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2022

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Identifying and quantifying cell death is the basis for all cell death research. Current methods for obtaining these quantitative measurements rely on established biomarkers, yet the marker-based approach suffers from limited marker specificity, high cost of reagents, lengthy sample preparation, and fluorescence imaging. Based on the morphological difference, we developed a Live, Apoptotic, and Necrotic Cell Explorer (LANCE) to categorize cell death status in a label-free manner, by incorporating machine learning and image processing. The LANCE workflow includes cropping individual cells from microscopic images having hundreds of cells, formation of an image database of around 5000 events, training and validation of the convolutional neural network models using multiple cell lines, and treatment conditions. With LANCE, we precisely categorized live, apoptotic, and necrotic cells with a high accuracy of 96.3 ± 0.5%. More importantly, the nondestructive label-free LANCE method allows for tracking time dynamics of the cell death process, which enhances the understanding of subtle cell death regulation at the molecular level. Hence, LANCE is a fast, low-cost, and nondestructive label-free method to distinguish cell status, which can be applied to cell death studies as well as many other biomedical applications.

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“…The MCF7 breast cancer tumor cell line described in the work was obtained from the American Type Culture Collection (ATCC) and maintained by the Institute of Biology and Experimental Medicine (IBYME), Buenos Aires, Argentina. This cell line was previously used in similar works [ 47 ]. This cell line is intended for laboratory research use only.…”

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confidence: 99%

GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices

et al. 2022

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Nowadays, image analysis has a relevant role in most scientific and research areas. This process is used to extract and understand information from images to obtain a model, knowledge, and rules in the decision process. In the case of biological areas, images are acquired to describe the behavior of a biological agent in time such as cells using a mathematical and computational approach to generate a system with automatic control. In this paper, MCF7 cells are used to model their growth and death when they have been injected with a drug. These mammalian cells allow understanding of behavior, gene expression, and drug resistance to breast cancer. For this, an automatic segmentation method called GEMA is presented to analyze the apoptosis and confluence stages of culture by measuring the increase or decrease of the image area occupied by cells in microfluidic devices. In vitro, the biological experiments can be analyzed through a sequence of images taken at specific intervals of time. To automate the image segmentation, the proposed algorithm is based on a Gabor filter, a coefficient of variation (CV), and linear regression. This allows the processing of images in real time during the evolution of biological experiments. Moreover, GEMA has been compared with another three representative methods such as gold standard (manual segmentation), morphological gradient, and a semi-automatic algorithm using FIJI. The experiments show promising results, due to the proposed algorithm achieving an accuracy above 90% and a lower computation time because it requires on average 1 s to process each image. This makes it suitable for image-based real-time automatization of biological lab-on-a-chip experiments.

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

Cited by 16 publications

References 37 publications

Machine learning-based detection of label-free cancer stem-like cell fate

Machine learning-based detection of label-free cancer stem-like cell fate

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

GEMA—An Automatic Segmentation Method for Real-Time Analysis of Mammalian Cell Growth in Microfluidic Devices

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