Septavera Sharvia

Data Availability Statement Data sharing not applicable to this article as no datasets were generated or analyzed during the current study. References 1, , and , “Systematic Review of Home Telemonitoring for Chronic Diseases: The Evidence Base,” Journal of the American Medical Informatics Association 14, no. 3 (2007): 269–277.

Abstract Computational analysis of fluorescent timelapse microscopy images at the single-cell level is a powerful approach to study cellular changes that dictate important cell fate decisions. Core to this approach is the need to generate reliable cell segmentations and classifications necessary for accurate quantitative analysis. Deep learning-based convolutional neural networks (CNNs) have emerged as a promising solution to these challenges.

Abstract Computational analysis of fluorescent timelapse microscopy images is a powerful approach to study biological processes in detail. Core to this approach is the generation of accurate single cell linages from cell segmentations for reliable quantitative analysis.