Automated Cell Foreground-Background Segmentation with Phase-Contrast Microscopy Images: An Alternative to Machine Learning Segmentation Methods with Small-Scale Data

Cell segmentation is a critical step for image-based experimental analysis. Existing cell segmentation methods are neither entirely automated nor perform well under basic laboratory microscopy. This study proposes an efficient and automated cell segmentation method involving morphological operations to automatically achieve cell segmentation for phase-contrast microscopes. Manual/visual counting of cell segmentation serves as the control group (156 images as ground truth) to evaluate the proposed method's performance. The proposed technology's adaptive performance is assessed at varying conditions, including artificial blurriness, illumination, and image size. Compared to the Trainable Weka Segmentation method, the Empirical Gradient Threshold method, and the ilastik segmentation software, the proposed method achieved better segmentation accuracy (dice coefficient: 90.07, IoU: 82.16%, and 6.51% as the average relative error on measuring cell area). The proposed method also has good reliability, even under unfavored imaging conditions at which manual labeling or human intervention is inefficient. Additionally, similar degrees of segmentation accuracy were confirmed when the ground truth data and the generated data from the proposed method were applied individually to train modified U-Net models (16848 images). These results demonstrated good accuracy and high practicality of the proposed cell segmentation method with phase-contrast microscopy image data.