Hemositometre üzerindeki lösemi kanser hücrelerinin otomatik segmentasyonu

Cell counting is used to determine cell number and cell density as a key step in the laboratory workflow. Determining cell density is very important both for accurate and precise diagnosis of diseases and drug experiments. Due to the high cost of the cell counting machines, specialists often count cells manually with a hemocytometer. Counting cells manually is both a laborious and time-consuming activity. In this study, semantic cell segmentation method based on deep learning is presented to count cells automatically. The data set that is analyzed in this study contains 468 light microscope images of HL60 leukemia cancer cells on the hemocytometer that are taken from cell culture. 421 of 468 images in the image set were reserved for use with k=5 fold cross-validation and 47 for model validation. Pixel accuracy and mean intersection over union (IoU) metrics were used to evaluate the training performance of the model built with U-Net. Hyper-parameters optimization was applied by Grid Search Algorithm. As a result, average pixel accuracy was achieved 98 percent and average IoU 87 percent. There are 636 cells in the test images and the number of cells acquired by using connected component analysis from the segmented results is 511. Thus, the cell detection rate was achieved 80 percent. By transferring the method developed in this study into application, experts can carry out cell counting procedure automatically without using a costly cell counting machine. Therefore, it is thought that the presented method will contribute both time and budget saving.