Biology subjects

Mashhadi, N.

Publications and source records attributed to Mashhadi, N..

3 recordsLinked to original sources

bioRxiv2024

Enhanced Cell Tracking Using A GAN-based Super-Resolution Video-to-Video Time-Lapse Microscopy Generative Model

Cells are among the most dynamic entities, constantly undergoing various processes such as growth, division, movement, and interaction with other cells as well as the environment. Time-lapse microscopy is central to capturing these dynamic behaviors, providing detailed temporal and spatial information that allows biologists to observe and analyze cellular activities in real-time. The analysis of time-lapse microscopy data relies on two fundamental tasks: cell segmentation and cell tracking. Integrating deep learning into bioimage analysis has revolutionized cell segmentation, producing models with high precision across a wide range of biological images. However, developing generalizable deep-learning models for tracking cells over time remains challenging due to the scarcity of large, diverse annotated datasets of time-lapse movies of cells. To address this bottleneck, we propose a GAN-based time-lapse microscopy generator, termed tGAN, designed to significantly enhance the quality and diversity of synthetic annotated time-lapse microscopy data. Our model features a dual-resolution architecture that adeptly synthesizes both low and high-resolution images, uniquely capturing the intricate dynamics of cellular processes essential for accurate tracking. We demonstrate the performance of tGAN in generating high-quality, realistic, annotated time-lapse videos. Our findings indicate that tGAN decreases dependency on extensive manual annotation to enhance the precision of cell tracking models for time-lapse microscopy.

systems biology↗

bioRxiv2023

Enhanced cell segmentation with limited annotated data using generative adversarial networks

The application of deep learning is rapidly transforming the field of bioimage analysis. While deep learning has shown great promise in complex microscopy tasks such as single-cell segmentation, the development of generalizable foundation deep learning segmentation models is hampered by the scarcity of large and diverse annotated datasets of cell images for training purposes. Generative Adversarial Networks (GANs) can generate realistic images that can potentially be easily used to train deep learning models without the generation of large manually annotated microscopy images. Here, we propose a customized CycleGAN architecture to train an enhanced cell segmentation model with limited annotated cell images, effectively addressing the challenge of paucity of annotated data in microscopy imaging. Our customized CycleGAN model can generate realistic synthetic images of cells with morphological details and nuances very similar to that of real images. This method not only increases the variability seen during training but also enhances the authenticity of synthetic samples, thereby enhancing the overall predictive accuracy and robustness of the cell segmentation model. Our experimental results show that our CycleGAN-based method significantly improves the performance of the segmentation model compared to conventional training techniques. Interestingly, we demonstrate that our model can extrapolate its knowledge by synthesizing imaging scenarios that were not seen during the training process. Our proposed customized CycleGAN method will accelerate the development of foundation models for cell segmentation in microscopy images.

systems biology↗

bioRxiv2021

DeepSea: An efficient deep learning model for automated cell segmentation and tracking

Dynamics and non-genetic heterogeneity are two fundamental characteristics of basic processes of life such as cell division or differentiation. Time-lapse microscopy is the only method that can directly capture the dynamics and heterogeneity of fundamental cellular processes at the singlecell level with high temporal resolution. Successful application of single-cell time-lapse microscopy requires automated segmentation and tracking of hundreds of individual cells over several time points. Recently, deep learning models have ushered in a new era in the quantitative analysis of microscopy images. However, integrated segmentation and tracking of single cells remain challenges for the analysis of time-lapse microscopy images. This work presents a versatile and trainable deep-learning software, termed DeepSea, that allows for both segmentation and tracking of single cells in sequences of phase-contrast live microscopy images. Our segmentation model can easily be trained to segment phase-contrast images of different cell types with higher precision than existing models. Our tracking model allows for quantification of dynamics of several cell biological features of individual cells, such as cell division cycle, mitosis, cell morphology, and cell size, with high precision using phase-contrast images. We showcase the application of DeepSea by analyzing cell size regulation in embryonic stem cells. Our findings show that embryonic stem cells exhibit cell size control in the G1 phase of the cell cycle despite their unusual fast division cycle. Our training dataset, user-friendly software, and code are available here https://deepseas.org.

systems biology↗