Cell quantification in digital contrast microscopy images with convolutional neural networks algorithm

Abstract

High Content Screening (HCS) combines high throughput techniques with the ability to generate cellular images of biological systems. The objective of this work is to evaluate the performance of predictive models using CNN to identify the number of cells present in digital contrast microscopy images obtained by HCS. One way to evaluate the algorithm was through the Mean Squared Error metric. The MSE was 4,335.99 in the A549 cell line, 25,295.23 in the Huh7 and 36,897.03 in the 3T3. After obtaining these values, different parameters of the models were changed to verify how they behave. By reducing the number of images, the MSE increased considerably, with the A549 cell line changing to 49,973.52, Huh7 to 79,473.88 and 3T3 to 52,977.05. Correlation analyzes were performed for the different models. In lineage A549, the best model showed a positive correlation with R = 0.953. In Huh7, the best correlation of the model was R = 0.821, it was also a positive correlation. In 3T3, the models showed no correlation, with the best model having R = 0.100. The models performed well in quantifying the number of cells, and the number and quality of the images interfered with this predictive ability.

Classification and counting of cells in brightfield microscopy images: an application of convolutional neural networks

“…This study is a continuation of a previous investigation into the quantification of cells in microscopy images 14 . Cell image quantification is fundamental in many biological and medical research tasks.…”

“…The present work was an offshoot of a previous work of the group published in Scientific Reports, in which we used CNN to quantify the number of cells present in the microscopy images 14 . Our regression algorithm showed good performance and accuracy in two of the three strains tested, demonstrating that not all cells can be equally quantified by this technique.…”