Preprocessing Optimization and Semantic Segmentation for Extraction of Cervical Intervertebral Disks from Videofluorography
Preprocessing Optimization and Semantic Segmentation for Extraction of Cervical Intervertebral Disks from Videofluorography
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DOI：
10.1109/icpr56361.2022.9956512
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发表时间：
2022-08
期刊：
影响因子：
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通讯作者：
H. Takizawa;Ayano Fujinaka;Erika Gunji;Kojiro Mekata;Hiroyuki Kudo
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文献类型：
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作者：
H. Takizawa;Ayano Fujinaka;Erika Gunji;Kojiro Mekata;Hiroyuki Kudo
语义分割是从医学图像中提取特定区域的最有力的工具之一。一些研究成功地通过结合预处理来提高SS的分割精度。本文提出了一种基于SS结合45个线性和非线性图像滤波器的吞咽困难诊断视频透视(VF)中颈椎间盘的提取方法。利用模拟退火法对图像滤波器的组合进行了优化。本研究将U-net、特征金字塔网络、LinkNet、金字塔场景分析网络和M-net五种完全卷积网络(FCN)应用于19例患者和39名健康受试者的VF数据集。未使用滤光片时，5种FCNs的平均F值分别为0.660、0.752、0.803、0.750和0.768，而使用滤光片时，FCNs的平均F值分别增加到0.747、0.794、0.813、0.765和0.799。实验结果表明，优化的图像滤波器组合能有效地提高FCNs的分割精度。
Semantic segmentation (SS) is one of the most powerful tools to extract particular regions from medical images. Several studies succeeded to increase the segmentation accuracy of SS by combining with preprocessing. The present study proposed an extraction method of cervical intervertebral disks from videofluorography (VF), which is commonly used for the diagnosis of dysphagia, based on SS combined with 45 linear and nonlinear image filters. The combination of the image filters was optimized by the simulated annealing algorithm. In this study, five fully convolutional networks (FCNs), i.e. U-Net, feature pyramid network, LinkNet, pyramid scene parsing network and M-Net, were applied to the VF dataset of 19 patients and 39 healthy participants. When the image filters were not used, the mean F measures of the five FCNs were 0.660, 0.752, 0.803, 0.750 and 0.768, respectively, whereas when used, they were increased to 0.747, 0.794, 0.813, 0.765 and 0.799, respectively. This experimental results demonstrated that the optimal combinations of image filters were effective to improve the segmentation accuracy of FCNs.