Automated label‐free detection of injured neuron with deep learning by two‐photon microscopy. Issue 1 (30th October 2019) - Wang, Shu; Lin, Bingbing; Lin, Guimin; Lin, Ruolan; Huang, Feng; Liu, Weilin; Wang, Xingfu; Liu, Xueyong; Zhang, Yu; Wang, Feng; Lin, Yuanxiang; Chen, Lidian; et al.
Abstract: Stroke is a significant cause of morbidity and long‐term disability globally. Detection of injured neuron is a prerequisite for defining the degree of focal ischemic brain injury, which can be used to guide further therapy. Here, we demonstrate the capability of two‐photon microscopy (TPM) to label‐freely identify injured neurons on unstained thin section and fresh tissue of rat cerebral ischemia‐reperfusion model, revealing definite diagnostic features compared with conventional staining images. Moreover, a deep learning model based on convolutional neural network is developed to automatically detect the location of injured neurons on TPM images. We then apply deep learning‐assisted TPM to evaluate the ischemic regions based on tissue edema, two‐photon excited fluorescence signal intensity, as well as neuronal injury, presenting a novel manner for identifying the infarct core, peri‐infarct area, and remote area. These results propose an automated and label‐free method that could provide supplementary information to augment the diagnostic accuracy, as well as hold the potential to be used as an intravital diagnostic tool for evaluating the effectiveness of drug interventions and predicting potential therapeutics. Abstract : In this study, we investigated the capability of two‐photon microscopy (TPM) to label‐freely identify injured neurons on unstained thin section and fresh tissue of rat cerebral ischemia‐reperfusion model. Moreover, a deep learning model based on convolutional neural network was developed to automatically detect the location of injured neurons. Finally, combining TPM with deep learning, we presented an automated and label‐free approach to evaluate the ischemic regions, which could improve diagnostic accuracy compared with standard histology.