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脊髓组织大规模相干反斯托克斯拉曼散射(CARS)图像中神经纤维分割与形态测量的自动化方法

Automated method for the segmentation and morphometry of nerve fibers in large-scale CARS images of spinal cord tissue.

作者信息

Bégin Steve, Dupont-Therrien Olivier, Bélanger Erik, Daradich Amy, Laffray Sophie, De Koninck Yves, Côté Daniel C

机构信息

Centre de recherche de l'Institut universitaire en santé mentale de Québec (CRIUSMQ), Université Laval, Québec, Canada ; Département de physique, génie physique et optique, Université Laval, Québec, Canada ; Centre d'optique, photonique et laser (COPL), Université Laval, Québec, Canada.

Centre de recherche de l'Institut universitaire en santé mentale de Québec (CRIUSMQ), Université Laval, Québec, Canada ; Centre d'optique, photonique et laser (COPL), Université Laval, Québec, Canada.

出版信息

Biomed Opt Express. 2014 Nov 5;5(12):4145-61. doi: 10.1364/BOE.5.004145. eCollection 2014 Dec 1.

Abstract

A fully automated method for large-scale segmentation of nerve fibers from coherent anti-Stokes Raman scattering (CARS) microscopy images is presented. The method is specifically designed for CARS images of transverse cross sections of nervous tissue but is also suitable for use with standard light microscopy images. After a detailed description of the two-part segmentation algorithm, its accuracy is quantified by comparing the resulting binary images to manually segmented images. We then demonstrate the ability of our method to retrieve morphological data from CARS images of nerve tissue. Finally, we present the segmentation of a large mosaic of CARS images covering more than half the area of a mouse spinal cord cross section and show evidence of clusters of neurons with similar g-ratios throughout the spinal cord.

摘要

本文提出了一种从相干反斯托克斯拉曼散射(CARS)显微镜图像中大规模分割神经纤维的全自动方法。该方法专为神经组织横切面的CARS图像设计,但也适用于标准光学显微镜图像。在详细描述了两部分分割算法后,通过将所得二值图像与手动分割图像进行比较来量化其准确性。然后,我们展示了我们的方法从神经组织的CARS图像中检索形态学数据的能力。最后,我们展示了对覆盖小鼠脊髓横截面积一半以上的大型CARS图像拼接图的分割,并显示了整个脊髓中具有相似g比率的神经元簇的证据。

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