Automated segmentation of foodborne bacteria from chicken rinse with hyperspectral microscope imaging and deep learning methods

Visible/near-infrared hyperspectral microscope imaging (HMI) has provided spectral-spatial features to identify pathogenic bacteria with high accuracy. But the bacterial detection with dark-field HMI requires accurate seg-mentation of single-cell bacteria from hyperspectral image (hypercube). In this study a robust technique was developed and evaluated to automatically segment single-cell pathogenic bacteria using deep learning and image processing. The proposed method consists of two steps as 1) bacterial segmentation with a deep learning model and 2) single-cell identification by ellipse fitting. Bacterial strains including Escherichia coli, Listeria, Salmonella, and Staphylococcus were prepared to obtain hyperspectral images of bacterial cells under different growth conditions with a Fabry-Perot Interferometer (FPI) HMI system. Based on the hypercube, four deep learning models including U-Net, residual U-Net (ResU-Net), attention gate residual U-Net (AGResU-Net), and attention gated recurrent residual U-Net (AGR2U-Net) were employed for bacterial cell segmentation. AGR2U-Net with deblurred input images and-1 image padding performed better than other models with 94.1% mean inter-section over union and visual inspection confirmed that segmented images with the model were identical to the ground-truth mask images. Also, ellipse fitting and goodness-of-fit evaluation were accurate in 97.4% of 6,426 examined cases. In addition, the robustness of the proposed method was confirmed because its segmentation accuracy and quality were moderately invariant with image blurriness and sample growth conditions. This ac-curate and robust auto-segmentation technique streamlined the detection of pathogenic bacteria with FPI-HMI by reducing processing time from raw hypercube acquisition to classification with 15 sec.