An automated in vitro wound healing microscopy image analysis approach utilizing U-net-based deep learning methodology

Background: The assessment of in vitro wound healing images is critical for determining the efficacy of the therapy-of-interest that may influence the wound healing process. Existing methods suffer significant limitations, such as their user dependency and time-consuming nature, and lack of sensitivity, and thus pave the way for automated analysis approaches. Methods: : Hereby, three structurally different variations of U-net architectures based on convolutional neural networks (CNN) were implemented for the segmentation of in vitro wound healing microscopy images. Developed models were fed using two independent datasets after applying a novel augmentation method based aim at the more sensitive analysis of edges after the preprocessing. Then, predicted masks were utilized for the accurate calculation of wound areas. Eventually, the therapy efficacy-indicator wound areas were thoroughly compared with current well-known tools such as ImageJ and TScratch. Results: : The average dice similarity coefficient (DSC) scores were obtained as 0.958∼0.968 for U-net-based deep learning models. The averaged absolute percentage errors of predicted wound areas to ground truth were 6.41%, 3.70%, and 3.73%, respectively for U-net, U-net++, and Attention U-net, while ImageJ and TScratch had huge averaged error rates of 22.59% and 33.88%, respectively. Conclusions: : Comparative analyses revealed that the developed models outperformed the conventional approaches in terms of analysis time and segmentation sensitivity. The developed models hold also great promise for the prediction of the in vitro wound area regardless of the therapy-of-interest, cell line, magnification of the microscope, or other application-dependent parameters.