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首页> 美国卫生研究院文献>Journal of Digital Imaging >A New Method for Automated Identification and Morphometry of Myelinated Fibers Through Light Microscopy Image Analysis
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A New Method for Automated Identification and Morphometry of Myelinated Fibers Through Light Microscopy Image Analysis

机译：通过光学显微镜图像分析自动识别有髓纤维的新方法

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摘要

Nerve morphometry is known to produce relevant information for the evaluation of several phenomena, such as nerve repair, regeneration, implant, transplant, aging, and different human neuropathies. Manual morphometry is laborious, tedious, time consuming, and subject to many sources of error. Therefore, in this paper, we propose a new method for the automated morphometry of myelinated fibers in cross-section light microscopy images. Images from the recurrent laryngeal nerve of adult rats and the vestibulocochlear nerve of adult guinea pigs were used herein. The proposed pipeline for fiber segmentation is based on the techniques of competitive clustering and concavity analysis. The evaluation of the proposed method for segmentation of images was done by comparing the automatic segmentation with the manual segmentation. To further evaluate the proposed method considering morphometric features extracted from the segmented images, the distributions of these features were tested for statistical significant difference. The method achieved a high overall sensitivity and very low false-positive rates per image. We detect no statistical difference between the distribution of the features extracted from the manual and the pipeline segmentations. The method presented a good overall performance, showing widespread potential in experimental and clinical settings allowing large-scale image analysis and, thus, leading to more reliable results.
机译：已知神经形态测量法可产生用于评估多种现象的相关信息，例如神经修复，再生，植入，移植，衰老和不同的人类神经病。手动形态测量是费力，繁琐，耗时的，并且容易出错。因此，在本文中，我们提出了一种在横截面光学显微镜图像中对有髓纤维的自动形态进行测定的新方法。本文使用了成年大鼠的喉返神经和成年豚鼠的前庭耳蜗神经的图像。提议的光纤分段管道基于竞争性聚类和凹度分析技术。通过比较自动分割与手动分割，对所提出的图像分割方法进行了评估。为了进一步考虑从分割图像中提取的形态特征来评估所提出的方法，对这些特征的分布进行了统计显着性差异测试。该方法实现了高整体灵敏度和每幅图像极低的假阳性率。我们检测到从手册中提取的特征的分布与管道细分之间没有统计差异。该方法具有良好的整体性能，在实验和临床环境中显示出广泛的潜力，可以进行大规模的图像分析，从而获得更可靠的结果。

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