Pulmonary Nodule Classification Using a Multiview Residual Selective Kernel Network

IF 2.9 2区 工程技术 Q2 RADIOLOGY, NUCLEAR MEDICINE & MEDICAL IMAGING Journal of Digital Imaging Pub Date : 2024-01-11 DOI:10.1007/s10278-023-00928-4
Herng-Hua Chang, Cheng-Zhe Wu, Audrey Haihong Gallogly
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Abstract

Lung cancer is one of the leading causes of death worldwide and early detection is crucial to reduce the mortality. A reliable computer-aided diagnosis (CAD) system can help facilitate early detection of malignant nodules. Although existing methods provide adequate classification accuracy, there is still room for further improvement. This study is dedicated to investigating a new CAD scheme for predicting the malignant likelihood of lung nodules in computed tomography (CT) images in light of a deep learning strategy. Conceived from the residual learning and selective kernel, we investigated an efficient residual selective kernel (RSK) block to handle the diversity of lung nodules with various shapes and obscure structures. Founded on this RSK block, we established a multiview RSK network (MRSKNet), to which three anatomical planes in the axial, coronal, and sagittal directions were fed. To reinforce the classification efficiency, seven handcrafted texture features with a filter-like computation strategy were explored, among which the homogeneity (HOM) feature maps are combined with the corresponding intensity CT images for concatenation input, leading to an improved network architecture. Evaluated on the public benchmark Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI) challenge database with ten-fold cross validation of binary classification, our experimental results indicated high area under receiver operating characteristic (AUC) and accuracy scores. A better compromise between recall and specificity was struck using the suggested concatenation strategy comparing to many state-of-the-art approaches. The proposed pulmonary nodule classification framework exhibited great efficacy and achieved a higher AUC of 0.9711. The association of handcrafted texture features with deep learning models is promising in advancing the classification performance. The developed pulmonary nodule CAD network architecture is of potential in facilitating the diagnosis of lung cancer for further image processing applications.

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使用多视角残差选择核网络进行肺结节分类
肺癌是导致全球死亡的主要原因之一,早期检测对降低死亡率至关重要。可靠的计算机辅助诊断(CAD)系统有助于及早发现恶性结节。虽然现有的方法能提供足够的分类准确性,但仍有进一步改进的空间。本研究致力于研究一种新的计算机辅助诊断方案,以深度学习策略为基础,预测计算机断层扫描(CT)图像中肺部结节的恶性可能性。在残差学习和选择性核的基础上,我们研究了一种高效的残差选择性核(RSK)块,以处理形状各异、结构模糊的肺结节。在此 RSK 块的基础上,我们建立了一个多视角 RSK 网络(MRSKNet),向其输入轴向、冠状和矢状三个解剖平面。为了提高分类效率,我们探索了七种采用滤波式计算策略的手工纹理特征,其中的同质性(HOM)特征图与相应的强度 CT 图像相结合,作为连接输入,从而改进了网络结构。我们的实验结果表明,接收器操作特征下面积(AUC)和准确率得分都很高,在公共基准肺图像数据库联盟和图像数据库资源倡议(LIDC-IDRI)挑战数据库上进行了评估,并对二元分类进行了十倍交叉验证。与许多最先进的方法相比,建议的连接策略在召回率和特异性之间实现了更好的折中。所提出的肺结节分类框架显示出巨大的功效,AUC 达到 0.9711。手工制作的纹理特征与深度学习模型的结合有望提高分类性能。所开发的肺结节 CAD 网络架构具有促进肺癌诊断的潜力,可用于进一步的图像处理应用。
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来源期刊
Journal of Digital Imaging
Journal of Digital Imaging 医学-核医学
CiteScore
7.50
自引率
6.80%
发文量
192
审稿时长
6-12 weeks
期刊介绍: The Journal of Digital Imaging (JDI) is the official peer-reviewed journal of the Society for Imaging Informatics in Medicine (SIIM). JDI’s goal is to enhance the exchange of knowledge encompassed by the general topic of Imaging Informatics in Medicine such as research and practice in clinical, engineering, and information technologies and techniques in all medical imaging environments. JDI topics are of interest to researchers, developers, educators, physicians, and imaging informatics professionals. Suggested Topics PACS and component systems; imaging informatics for the enterprise; image-enabled electronic medical records; RIS and HIS; digital image acquisition; image processing; image data compression; 3D, visualization, and multimedia; speech recognition; computer-aided diagnosis; facilities design; imaging vocabularies and ontologies; Transforming the Radiological Interpretation Process (TRIP™); DICOM and other standards; workflow and process modeling and simulation; quality assurance; archive integrity and security; teleradiology; digital mammography; and radiological informatics education.
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