Cell segmentation from wide-field light microscopy images using CNNs

Abstract

Image object segmentation allows localising the region of interest in the image (ROI) and separating the foreground from the background. Cell detection and segmentation are the primary and critical steps in microscopy image analysis. Analysing microscopy images allows us to extract vital information about the cells, including their morphology, size, and life cycle. On the other hand, living cell segmentation is challenging due to the complexity of these datasets. This research focused on developing Artificial Intelligence/Machine Learning methods of single- and multi-class segmentation of living cells. For this study, the Negroid cervical epithelioid carcinoma HeLa line was chosen as the oldest, immortal, and most widely used model cell line. Several time-lapse image series of living HeLa cells were captured using a high-resolved wide-field transmitted/reflected light microscope (custom-made for the Institute of Complex System, Nové Hrady, Czech Republic) to observe micro-objects and cells. Employing a telecentric objective with a high-resolution camera with a large sensor size allows us to achieve a high level of detail and sharper borders in large microscopy images. The collected time-lapse images were calibrated and denoised in the pre-processing step. The data sets collected under the transmission microscope setup were analyzed using a simple U-Net, Attention U-Net, and Residual Attention U-Net to achieve the best single-class semantic segmentation result. The data sets collected under the reflection microscope setup were analyzed using hybrid U-Net methods, including Vgg19-Unet, Inception-Unet, and ResNet34-Unet, to achieve the most precise multi-class segmentation result.