结合自动分割和放射组学的胶质瘤计算机辅助分级。

IF 3.3 Q2 ENGINEERING, BIOMEDICAL
International Journal of Biomedical Imaging Pub Date : 2018-05-08 eCollection Date: 2018-01-01 DOI:10.1155/2018/2512037
Wei Chen, Boqiang Liu, Suting Peng, Jiawei Sun, Xu Qiao
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引用次数: 60

摘要

胶质瘤是最常见的原发性脑肿瘤,其客观分级对治疗具有重要意义。本文提出了一种将自动分割与放射组学相结合的神经胶质瘤计算机辅助自动诊断方法,可提高诊断能力。MRI数据包含220个高级别胶质瘤和54个低级别胶质瘤,用于评估我们的系统。训练了一个多尺度三维卷积神经网络来分割整个肿瘤区域。广泛的放射学特征包括一阶特征、形状特征和纹理特征。采用递归特征消去的支持向量机进行特征选择,构建了一个具有5倍交叉验证的极端梯度增强分类器的CAD系统,用于胶质瘤分级。我们的CAD系统对胶质瘤分级非常有效,准确率为91.27%,加权宏观精度为91.27%,加权宏观召回率为91.27%,加权宏观f1评分为90.64%。这表明所提出的CAD系统可以帮助放射科医生对胶质瘤进行高精度分级,具有临床应用的潜力。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics.

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics.

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics.

Computer-Aided Grading of Gliomas Combining Automatic Segmentation and Radiomics.

Gliomas are the most common primary brain tumors, and the objective grading is of great importance for treatment. This paper presents an automatic computer-aided diagnosis of gliomas that combines automatic segmentation and radiomics, which can improve the diagnostic ability. The MRI data containing 220 high-grade gliomas and 54 low-grade gliomas are used to evaluate our system. A multiscale 3D convolutional neural network is trained to segment whole tumor regions. A wide range of radiomic features including first-order features, shape features, and texture features is extracted. By using support vector machines with recursive feature elimination for feature selection, a CAD system that has an extreme gradient boosting classifier with a 5-fold cross-validation is constructed for the grading of gliomas. Our CAD system is highly effective for the grading of gliomas with an accuracy of 91.27%, a weighted macroprecision of 91.27%, a weighted macrorecall of 91.27%, and a weighted macro-F1 score of 90.64%. This demonstrates that the proposed CAD system can assist radiologists for high accurate grading of gliomas and has the potential for clinical applications.

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来源期刊
CiteScore
12.00
自引率
0.00%
发文量
11
审稿时长
20 weeks
期刊介绍: The International Journal of Biomedical Imaging is managed by a board of editors comprising internationally renowned active researchers. The journal is freely accessible online and also offered for purchase in print format. It employs a web-based review system to ensure swift turnaround times while maintaining high standards. In addition to regular issues, special issues are organized by guest editors. The subject areas covered include (but are not limited to): Digital radiography and tomosynthesis X-ray computed tomography (CT) Magnetic resonance imaging (MRI) Single photon emission computed tomography (SPECT) Positron emission tomography (PET) Ultrasound imaging Diffuse optical tomography, coherence, fluorescence, bioluminescence tomography, impedance tomography Neutron imaging for biomedical applications Magnetic and optical spectroscopy, and optical biopsy Optical, electron, scanning tunneling/atomic force microscopy Small animal imaging Functional, cellular, and molecular imaging Imaging assays for screening and molecular analysis Microarray image analysis and bioinformatics Emerging biomedical imaging techniques Imaging modality fusion Biomedical imaging instrumentation Biomedical image processing, pattern recognition, and analysis Biomedical image visualization, compression, transmission, and storage Imaging and modeling related to systems biology and systems biomedicine Applied mathematics, applied physics, and chemistry related to biomedical imaging Grid-enabling technology for biomedical imaging and informatics
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