Comparison of Flow Cytometry and Image-Based Screening for Cell Cycle Analysis

Matuszewski, Damian J.; Sintorn, Ida‐Maria; Carreras-Puigvert, Jordi; Wählby, Carolina

doi:10.1007/978-3-319-41501-7_70

Cited by 5 publications

(5 citation statements)

References 6 publications

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“…However, this method lacks the temporal component of cell-cycle progression. As reported in [8,9], DNA content could be used for identifying cell cycle phases and perfect segmentation of the cell nucleus is the major requirement for this method. For cell-cycle classification, Ferro et al [10] proposed feature extraction from fluorescence images followed by K-means clustering to identify cell-cycle stages.…”

Section: Related Workmentioning

confidence: 99%

Cell Cycle State Prediction Using Graph Neural Networks

Acharya,

Ganguly,

Sarkar

et al. 2024

Preprint

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Mitosis is a crucial process ensuring the faithful transmission of the genetic information stored in the cell nucleus. Aberrations in this intricate process pose a significant threat to an organism’s health, leading to conditions like cancer and various diseases. Hence, the study of mitosis holds paramount importance. Recent investigations have involved manual and semiautomated analyses of time-lapse microscopy images to understand mitosis better. This paper introduces an approach for predicting mitosis stages, employing a Convolutional Neural Network (CNN) as the initial feature extractor, followed by a Graph Neural Network (GNN) for predicting cell cycle states. A distinctive timestamp is incorporated into the feature vectors, treating this information as a graph to leverage internal interactions for predicting the subsequent cell state. To assess performance, experiments were conducted on three datasets, demonstrating that our method exhibits comparable efficacy to state-of-the-art techniques.

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Section: Related Workmentioning

confidence: 99%

Cell Cycle State Prediction Using Graph Neural Networks

Acharya,

Ganguly,

Sarkar

et al. 2024

Preprint

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“…Flow cytometry is another advancement in the study of cellcycle [11], but it lacks the temporal component of cell-cycle progression, especially in the short time of mitotic sub-phases, potentially missing valuable information. According to [12, 13] DNA content could be used for identifying the phases of the cell. These methods rely on perfect segmentation of the cell nucleus for accurate identification.…”

Section: Introductionmentioning

confidence: 99%

End-to-End Classification of Cell-Cycle Stages With Center-Cell Focus Tracker Using Recurrent Neural Networks

Jose

Roy

Eschweiler

et al. 2022

Preprint

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Cell division, or mitosis, guarantees the accurate inheritance of the genomic information kept in the cell nucleus. Malfunctions in this process cause a threat to the health and life of the organism, including cancer and other manifold diseases. It is therefore crucial to study in detail the cell-cycle in general and mitosis in particular. Consequently, a large number of manual and semi-automated time-lapse microscopy image analyses of mitosis have been carried out in recent years. In this paper, we propose a method for automatic detection of cell-cycle stages using a recurrent neural network (RNN). An end-to-end model with center-cell focus tracker loss, and classification loss is trained. The evaluation was conducted on two time-series datasets, with 6-stages and 3-stages of cell splitting labeled. The frame-to-frame accuracy was calculated and precision, recall, and F1 Score were measured for each cell-cycle stage. We also visualized the learned feature space. Image reconstruction from the center-cell focus module was performed which shows that the network was able to focus on the center cell and classify it simultaneously. Our experiments validate the superior performance of the proposed network compared to a classifier baseline.

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“…According to Roukos et al [2] and Damian et al [3], the cell cycle stage of a single cell can be determined by calculating its DNA content; however, these methods rely on accurate results from the segmentation of the nucleus. Schönenberger et al [4] studied the cell cycle by labeling proliferating cell nuclear antigen (PCNA).…”

Section: Introductionmentioning

confidence: 99%

An Imbalanced Image Classification Method for the Cell Cycle Phase

2021

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The cell cycle is an important process in cellular life. In recent years, some image processing methods have been developed to determine the cell cycle stages of individual cells. However, in most of these methods, cells have to be segmented, and their features need to be extracted. During feature extraction, some important information may be lost, resulting in lower classification accuracy. Thus, we used a deep learning method to retain all cell features. In order to solve the problems surrounding insufficient numbers of original images and the imbalanced distribution of original images, we used the Wasserstein generative adversarial network-gradient penalty (WGAN-GP) for data augmentation. At the same time, a residual network (ResNet) was used for image classification. ResNet is one of the most used deep learning classification networks. The classification accuracy of cell cycle images was achieved more effectively with our method, reaching 83.88%. Compared with an accuracy of 79.40% in previous experiments, our accuracy increased by 4.48%. Another dataset was used to verify the effect of our model and, compared with the accuracy from previous results, our accuracy increased by 12.52%. The results showed that our new cell cycle image classification system based on WGAN-GP and ResNet is useful for the classification of imbalanced images. Moreover, our method could potentially solve the low classification accuracy in biomedical images caused by insufficient numbers of original images and the imbalanced distribution of original images.

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Comparison of Flow Cytometry and Image-Based Screening for Cell Cycle Analysis

Cited by 5 publications

References 6 publications

Cell Cycle State Prediction Using Graph Neural Networks

Cell Cycle State Prediction Using Graph Neural Networks

End-to-End Classification of Cell-Cycle Stages With Center-Cell Focus Tracker Using Recurrent Neural Networks

An Imbalanced Image Classification Method for the Cell Cycle Phase

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