Automated Cell Counting using Image Processing

Abstract

Manual cell counting using Hemocytometer is commonly used to quantify cells, as it is an inexpensive and versatile method. However, it is labour-intensive, tedious, and time-consuming. On the other hand, most automated cell counting methods are expensive and require experts to operate. Thus, the use of image analysis software allows one to access low-cost but robust automated cell counting. This study explores the advanced setting of image processing software to obtain routes with the highest counting accuracy. The results show the effectiveness of advanced settings in CellProfiler for counting cells from synthetic images. Two routes were found to give the highest performance, with average image and cell accuracies of 85% and 99.8%, respectively, and the highest F1 score of 0.83. However, the two routes were unable to correctly determine the exact number of cells on the histology images, albeit giving a respectable cell accuracy of 79.6%. Further investigation has shown that CellProfiler is able to correctly identify the bulk of the cells within the histology images. Good image quality with high focus and less blur was identified as the key to successful image-based cell counting. To further enhance the accuracy, other modules can be included to further segment an object hence improving the number of objects identified. Future work can focus on evaluating the robustness of the routes by comparing them with other methods and validating with the manual cell counting method.