U-Net-Based HeLa Cell Segmentation with Zero Manual Labeling Using DBSCAN-Generated Annotations

Cell segmentation is essential for obtaining quantitative data from biological microscopy images. Over the past decade, multiple supervised deep learning methods have been developed to aid in this process. However, these processes require extensive human-annotated labels to train the algorithms. To address this, we propose a novel pipeline for segmentation of images having higher cell-density. We implemented unsupervised DBSCAN to segment the low density (sparse cell) images. The segmented outputs from these sparse cell images were then used as labeled data to improve the segmentation of high-density (crowded cell) images using a supervised U-Net framework. The proposed pipeline has zero reliance on manual annotation and we achieved accurate segmentation of dense cells. This can be used for classification of images according to their packing density and decode the cell communication by computational modeling.