Automated label‐free detection of injured neuron with deep learning by two‐photon microscopy

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. This article is protected by copyright. All rights reserved.

[1]  R. J. Mullen,et al.  NeuN, a neuronal specific nuclear protein in vertebrates. , 1992, Development.

[2]  T. Murphy,et al.  Two-Photon Imaging during Prolonged Middle Cerebral Artery Occlusion in Mice Reveals Recovery of Dendritic Structure after Reperfusion , 2008, The Journal of Neuroscience.

[3]  T. Murphy,et al.  In Vivo 2-Photon Imaging of Fine Structure in the Rodent Brain: Before, During, and After Stroke , 2010, Stroke.

[4]  Jean-Claude Baron,et al.  Selective Neuronal Loss in Ischemic Stroke and Cerebrovascular Disease , 2014, Journal of cerebral blood flow and metabolism : official journal of the International Society of Cerebral Blood Flow and Metabolism.

[5]  R. Macwalter,et al.  The cost of cerebral ischaemia , 2008, Neuropharmacology.

[6]  P. Weinstein,et al.  Reversible middle cerebral artery occlusion without craniectomy in rats. , 1989, Stroke.

[7]  Chia-Yuan Chang,et al.  Fast volumetric imaging with patterned illumination via digital micro-mirror device-based temporal focusing multiphoton microscopy , 2016, Biomedical optics express.

[8]  Weilin Wu,et al.  Rapid, label‐free identification of cerebellar structures using multiphoton microscopy , 2017, Journal of biophotonics.

[9]  Marc Fisher,et al.  Identifying and utilizing the ischemic penumbra , 2012, Neurology.

[10]  Hans Georg Breunig,et al.  Assessment of Human Corneas Prior to Transplantation Using High-Resolution Two-Photon Imaging. , 2018, Investigative ophthalmology & visual science.

[11]  Gerhard Schroth,et al.  Imaging of Acute Ischemic Stroke , 2014, European Neurology.

[12]  Xiaoqin Zhu,et al.  Spatial and temporal identification of cerebral infarctions based on multiphoton microscopic imaging. , 2018, Biomedical optics express.

[13]  B. van Ginneken,et al.  Deep learning as a tool for increased accuracy and efficiency of histopathological diagnosis , 2016, Scientific Reports.

[14]  W. Denk,et al.  Two-photon laser scanning fluorescence microscopy. , 1990, Science.

[15]  Nozomi Nishimura,et al.  Three-photon excited fluorescence imaging of unstained tissue using a GRIN lens endoscope , 2013, Biomedical optics express.

[16]  Lin Fang,et al.  Effects of CO2 pneumoperitoneum on proliferation, apoptosis, and migration of gastrointestinal stromal tumor cells , 2019, Surgical Endoscopy.

[17]  S. Yamaguchi,et al.  Transplantation of human mesenchymal stem cells promotes functional improvement and increased expression of neurotrophic factors in a rat focal cerebral ischemia model , 2009, Journal of neuroscience research.

[18]  Antoine M. Hakim.  Ischemic penumbra , 1998, Neurology.

[19]  Turgay Dalkara,et al.  Apoptotic and Necrotic Death Mechanisms Are Concomitantly Activated in the Same Cell After Cerebral Ischemia , 2004, Stroke.

[20]  James G. Fujimoto,et al.  Assessment of breast pathologies using nonlinear microscopy , 2014, Proceedings of the National Academy of Sciences.

[21]  T. Johnson,et al.  Detection of human brain tumor infiltration with quantitative stimulated Raman scattering microscopy , 2015, Science Translational Medicine.

[22]  Peter T C So,et al.  Scanless volumetric imaging by selective access multifocal multiphoton microscopy. , 2019, Optica.

[23]  Misha B. Ahrens,et al.  Visualizing Whole-Brain Activity and Development at the Single-Cell Level Using Light-Sheet Microscopy , 2015, Neuron.

[24]  W. Webb,et al.  Live tissue intrinsic emission microscopy using multiphoton-excited native fluorescence and second harmonic generation , 2003, Proceedings of the National Academy of Sciences of the United States of America.

[25]  Jian Sun,et al.  Deep Residual Learning for Image Recognition , 2015, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[26]  David A. Boas,et al.  In vivo imaging of cerebral energy metabolism with two-photon fluorescence lifetime microscopy of NADH , 2013, Biomedical optics express.

[27]  Geoffrey E. Hinton,et al.  Deep Learning , 2015, Nature.

[28]  Stephen A. Boppart,et al.  Stain-free histopathology by programmable supercontinuum pulses , 2016, Nature Photonics.

[29]  S. Warach,et al.  Imaging of acute stroke , 2010, Nature Reviews Neurology.

[30]  Heping Cheng,et al.  Fast high-resolution miniature two-photon microscopy for brain imaging in freely behaving mice , 2017, Nature Methods.

[31]  Yuanxiang Lin,et al.  Automatic and label‐free identification of blood vessels in gliomas using the combination of multiphoton microscopy and image analysis , 2019, Journal of biophotonics.

[32]  Robert W Boyd,et al.  Automated classification of multiphoton microscopy images of ovarian tissue using deep learning , 2018, Journal of biomedical optics.

[33]  D. Choi,et al.  Ischemia-induced neuronal apoptosis , 1996, Current Opinion in Neurobiology.

[34]  Nasir M. Rajpoot,et al.  Locality Sensitive Deep Learning for Detection and Classification of Nuclei in Routine Colon Cancer Histology Images , 2016, IEEE Trans. Medical Imaging.