A deep learning approach for automation in neurite tracing and cell size estimation from differential contrast images under healthy and hypoxic condition.

Chronic hypoxia is known to be a major cause of neurite length retraction followed be degeneration. Specifically, laser scanning confocal microscopy (LSCM) based-contrast imaging is used for monitoring neuronal morphology under hypoxic condition. Although imaging of neurons using LSCM via differential contrast imaging (DIC) is a powerful tool to identify the neuronal states under degenerative condition, fully automated quantification of neurite length and cell shape remains challenging. In this context, we propose an integrated framework that combines panorama imaging of neuronal morphology using LSCM, and deep learning model that allows automated tracing of neurites and cell shape. First, we establish an in vitro hypoxic model using cobalt chloride treatment of N2A cells and perform the large-scale imaging using DIC optics. Next, we tested the performance of U-Net, U-Net++ and FCN architecture using DIC images, where U-Net and U-Net++ demonstrates robustness and accuracy in tracing neurite length and segmentation of cell shape. The result shows that the U-Net++ is able to depict the difference in cell size and neurite length for control and chronic hypoxic condition. The proposed method was also validated and compared with other CNN models including FCN and U-Net. Moreover, the analysis indicates a significant alteration of cell shape and neurite length under hypoxic condition via deep-learning based automated cell segmentation.Clinical Relevance-The proposed framework assumes importance where quantification of neurite length and cell shape from a large dataset remains challenging due to time-consuming manual segmentation by experts. Specially, the framework based on labeling of a small dataset (15-20 images) can be used to identify the neuronal state under neurodegeneration and image-based assessment of neuroprotective drugs.