CellCountCV—A Web-Application for Accurate Cell Counting and Automated Batch Processing of Microscopic Images Using Fully Convolutional Neural Networks

Антонец, Денис; Russkikh, Nikolay; Sanchez, Antoine; Kovalenko, V.R.; Bairamova, Elvira; Shtokalo, Dmitry; Medvedev, S. P.; Zakian, Suren M.

doi:10.3390/s20133653

Cited by 6 publications

(3 citation statements)

References 23 publications

(26 reference statements)

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the past several automated algorithms have been developed for analyzing microscopic images, counting of cells and quanti cation of different cellular phenomena [9][10][11][12][13][14][15][16]. Segmentation and detection are the two major requirements for analyzing microscopic images.…”

Section: Introductionmentioning

confidence: 99%

Investigation of time dependent growth of HepG2 cancerous cells using deep learning and shape metrics

Ronickom

2023

Preprint

View full text Add to dashboard Cite

Study of growth of Human hepatocellular carcinoma cells (HepG2) cells provide useful information for clinical study of megestrol acetate for the treatment of Hepatocellular carcinoma. In this study, we analyzed the growth of HepG2 liver cancerous cells using image processing methods. Initially, the HepG2 cells were cultured and microscopic images were captured in bright field mode at time of seeding (00 h) followed by 06 h and 18 h. We segmented the cells using Tsallis and deep learning methods and the average size of colonies were calculated using shape metrics. Finally, we correlated the cell density obtained using MTT assay with the average size of colonies calculated from the Tsallis and deep learning segmented images. Results show that deep learning methods were able to segment the cells more precisely than Tsallis method. The average colony area calculated from the deep learning segmented images increases with time and concentration. The cell growth and adhesion pattern measured by deep leaning method showed good consistency with spectroscopic observations. The process pipeline provides a new way to assess cell adhesion and proliferation with capabilities in measuring their occupied surface area. The framework documented can be a promising tool to automate cancerous cell growth by evaluating average colony size for studying normal and pathological conditions.

show abstract

Section: Introductionmentioning

confidence: 99%

Investigation of time dependent growth of HepG2 cancerous cells using deep learning and shape metrics

Ronickom

2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Existing platforms like Fiji, Metamorph and many more, while useful, lack the versatility required for diverse biological analyses. Recent developments have seen the emergence of tools specifically designed for exocytosis event analysis (8,11,12). Our work diverges from these complex methodologies, aiming instead for a simplified, scalable, and rapid detection of exocytosis.…”

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Algorithm for automatic detection of insulin granule exocytosis in human pancreatic β-cells

Makam,

Dubey,

Maharana

et al. 2023

Preprint

View full text Add to dashboard Cite

Image processing and analysis are two significant areas that are highly important for interpreting enormous amounts of data obtained from microscopy-based experiments. Several image analysis tools exist for the general detection of fundamental cellular processes, but tools to detect highly distinct cellular functions are few. One such process is exocytosis, which involves the release of vesicular content out of the cell. The size of the vesicles and the inherent differences in the imaging parameters demand specific analysis platforms for detecting exocytosis. In this direction, we have developed an image-processing algorithm based on Lagrangian particle tracking. The tool was developed to ensure that there is efficient detection of punctate structures initially developed by mathematical equations, fluorescent beads and cellular images with fluorescently labelled vesicles that can exocytose. The detection of these punctate structures using the tool was compared with other existing tools, such as find maxima in ImageJ and manual detection. The tool not only met the precision of existing solutions but also expedited the process, resulting in a more time-efficient solution. During exocytosis, there is a sudden increase in the intensity of the fluorescently labelled vesicles that look like punctate structures. The algorithm precisely locates the vesicles’ coordinates and quantifies the variations in their respective intensities. Subsequently, the algorithm processes and retrieves pertinent information from large datasets surpassing that of conventional methods under our evaluation, affirming its efficacy. Furthermore, the tool exhibits adaptability for the image analysis of diverse cellular processes, requiring only minimal modifications to ensure accurate detection of exocytosis.

show abstract

Algorithm for semi-automatic detection of insulin granule exocytosis in human pancreatic β-cells

Makam,

Dubey,

Maharana

et al. 2024

Heliyon

View full text Add to dashboard Cite

CellCountCV—A Web-Application for Accurate Cell Counting and Automated Batch Processing of Microscopic Images Using Fully Convolutional Neural Networks

Cited by 6 publications

References 23 publications

Investigation of time dependent growth of HepG2 cancerous cells using deep learning and shape metrics

Investigation of time dependent growth of HepG2 cancerous cells using deep learning and shape metrics

Algorithm for automatic detection of insulin granule exocytosis in human pancreatic β-cells

Algorithm for semi-automatic detection of insulin granule exocytosis in human pancreatic β-cells

Contact Info

Product

Resources

About