Identification and segmentation of myelinated nerve fibers in a cross-sectional optical microscopic image using a deep learning model
Identification and segmentation of myelinated nerve fibers in a cross-sectional optical microscopic image using a deep learning model
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DOI：
10.1016/j.jneumeth.2017.08.014
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发表时间：
2017-11-01
影响因子：
3
通讯作者：
Shimizu, Jun
中科院分区：
文献类型：
--
作者：
Naito, Tatsuhiko;Nagashima, Yu;Shimizu, Jun
背景：周围神经有髓神经纤维的横截面光学显微镜图像的形态计量学分析是有价值的。已经报道了几种用于神经纤维识别和分割的自动化方法。本文提出了一种使用卷积神经网络（CNN）深度学习模型的新方法。方法：该方法包括四个步骤：归一化、聚类分割、有髓神经纤维识别和聚类分裂。对归一化后的样本图像进行聚类分割，将其分割为单个目标.每个对象都被应用到CNN深度学习模型中，该模型将有髓鞘神经纤维标记为阳性，将其他结构标记为阴性。只有积极的才能进入下一步。为了预训练模型，使用了来自39个样本的70，000个阳性和阴性数据。使用不属于训练集的10个样本评估所提出的算法的准确性。p值为
Background: The morphometric analysis of myelinated nerve fibers of peripheral nerves in cross-sectional optical microscopic images is valuable. Several automated methods for nerve fiber identification and segmentation have been reported. This paper presents a new method that uses a deep learning model of a convolutional neural network (CNN). We tested it for human sural nerve biopsy images.Methods: The method comprises four steps: normalization, clustering segmentation, myelinated nerve fiber identification, and clump splitting. A normalized sample image was separated into individual objects with clustering segmentation. Each object was applied to a CNN deep learning model that labeled myelinated nerve fibers as positive and other structures as negative. Only positives proceeded to the next step. For pretraining the model, 70,000 positive and negative data each from 39 samples were used. The accuracy of the proposed algorithm was evaluated using 10 samples that were not part of the training set. A P-value of