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

Microscopy is integral to medical research, facilitating the exploration of various biological questions, notably cell quantification. However, this process's time-consuming and error-prone nature, attributed to human intervention or automated methods usually applied to fluorescent images, presents challenges. In response, machine learning algorithms have been integrated into microscopy, automating tasks and constructing predictive models from vast datasets. These models adeptly learn representations for object detection, image segmentation, and target classification. An advantageous strategy involves utilizing unstained images, preserving cell integrity and enabling morphology-based classification-something hindered when fluorescent markers are used. The aim is to introduce a model proficient in classifying distinct cell lineages in digital contrast microscopy images. Additionally, the goal is to create a predictive model identifying lineage and determining optimal quantification of cell numbers. Employing a CNN machine learning algorithm, a classification model predicting cellular lineage achieved a remarkable accuracy of 93%, with ROC curve results nearing 1.0, showcasing robust performance. However, some lineages, namely SH-SY5Y (78%), HUH7_mayv (85%), and A549 (88%), exhibited slightly lower accuracies. These outcomes not only underscore the model's quality but also emphasize CNNs' potential in addressing the inherent complexities of microscopic images.

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

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.