Carlos Chagas Institute, Curitiba, PR, Brazil.
Department of Electrical Engineering, Federal University of Minas Gerais, Belo Horizonte, MG, Brazil.
Sci Rep. 2023 Feb 14;13(1):2596. doi: 10.1038/s41598-023-29694-7.
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.
高内涵筛选 (HCS) 将高通量技术与生成生物系统细胞图像的能力相结合。这项工作的目的是评估使用 CNN 识别 HCS 获得的数字对比度显微镜图像中存在的细胞数量的预测模型的性能。评估算法的一种方法是通过均方误差 (MSE) 度量。在 A549 细胞系中，MSE 为 4335.99，在 Huh7 中为 25295.23，在 3T3 中为 36897.03。获得这些值后，改变模型的不同参数以验证它们的行为方式。通过减少图像数量，MSE 大幅增加，A549 细胞系变为 49973.52，Huh7 变为 79473.88，3T3 变为 52977.05。对不同模型进行了相关性分析。在 A549 系中，最佳模型表现出与 R=0.953 的正相关。在 Huh7 中，模型的最佳相关性为 R=0.821，也是正相关。在 3T3 中，模型没有相关性，最佳模型的 R=0.100。模型在定量细胞数量方面表现良好，图像的数量和质量会干扰这种预测能力。
