Segmentation of Bacterial Cells in Biofilms Using an Overlapped Ellipse Fitting Technique

Accurate detection and segmentation of bacterial cells in the microscopy images of a biofilm is essential to develop technologies to resist microbial corrosion. The traditional approach of manually identifying cell regions in microscopy images is a time-consuming and error-prone task. Nonetheless, many of the existing approaches, including automated systems adopting advanced machine learning models, find it challenging to detect and segment cell instances in clustered biofilms where cells are overlapping and touching each other. In this paper, we develop a method to segment and extract the size properties of all cells. The proposed method consists of two stages, a semantic segmentation stage based on a U-Net architecture followed by a region-based ellipse fitting technique for instance segmentation and size property extraction. We compared the performance of our approach against a widely used object segmentation approach namely Mask R-CNN and found that our algorithm outperformed Mask R-CNN in terms of the segmentation efficiency and cell size estimation for images of Bacillus subtilis biofilms.