Surgical Photonics and Engineering Laboratory, Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts.
Center for Advanced Imaging, Harvard University, Cambridge, Massachusetts.
Muscle Nerve. 2020 Jul;62(1):137-142. doi: 10.1002/mus.26895. Epub 2020 May 8.
Conventional processing of nerve for histomorphometry is resource-intensive, precluding use in intraoperative assessment of nerve quality during nerve transfer procedures. Stimulated Raman scattering (SRS) microscopy is a label-free technique that enables rapid and high-resolution histology.
Segments of healthy murine sciatic nerve, healthy human obturator nerve, and human cross-facial nerve autografts were imaged on a custom SRS microscope. Myelinated axon quantification was performed through segmentation using a random forest machine learning algorithm in commercial software.
High contrast, high-resolution imaging of nerve morphology was obtained with SRS imaging. Automated myelinated axon quantification from cross-sections of healthy human nerve imaged using SRS was achieved.
Herein, we demonstrate the use of a label-free technique for rapid imaging of murine and human peripheral nerve cryosections. We illustrate the potential of this technique to inform intraoperative decision-making through rapid automated quantification of myelinated axons using a machine learning algorithm.
神经的常规形态计量学处理过程需要耗费大量资源,因此无法在神经转移手术中用于术中评估神经质量。受激拉曼散射(SRS)显微镜是一种无需标记的技术,可实现快速和高分辨率的组织学成像。
使用定制的 SRS 显微镜对健康的小鼠坐骨神经、健康的人类闭孔神经和人体面横神经自体移植物的片段进行成像。通过使用商业软件中的随机森林机器学习算法对髓鞘轴突进行分割,从而实现髓鞘轴突的定量。
通过 SRS 成像获得了具有高对比度和高分辨率的神经形态图像。使用 SRS 对健康人神经的横截面进行成像,实现了自动对髓鞘轴突进行定量。
本文展示了一种用于快速成像鼠和人周围神经冷冻切片的无标记技术。我们通过使用机器学习算法对髓鞘轴突进行快速自动定量,说明了该技术在术中决策中的潜在应用。