期刊
IEEE JOURNAL OF BIOMEDICAL AND HEALTH INFORMATICS
卷 23, 期 6, 页码 2526-2536出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/JBHI.2018.2890045
关键词
Computed tomography; Lung; Feature extraction; Diseases; Correlation; Deep learning; Biomedical imaging; Emphysema classification; deep learning; quantification analysis; multi-scale; differential excitation component
类别
资金
- Major Scientific Research Project of Zhejiang Lab [2018DG0ZX01]
- Key Science and Technology Innovation Support Program of Hangzhou [20172011A038]
- Japanese Ministry for Education, Science, Culture and Sports (MEXT) [18H03267, 17H00754]
- Grants-in-Aid for Scientific Research [17H00754] Funding Source: KAKEN
Automated tissue classification is an essential step for quantitative analysis and treatment of emphysema. Although many studies have been conducted in this area, there still remain two major challenges. First, different emphysematous tissue appears in different scales, which we call inter-class variations. Second, the intensities of CT images acquired from different patients, scanners or scanning protocols may vary, which we call intra-class variations. In this paper, we present a novel multi-scale residual network with two channels of raw CT image and its differential excitation component. We incorporate multi-scale information into our networks to address the challenge of inter-class variations. In addition to the conventional raw CT image, we use its differential excitation component as a pair of inputs to handle intra-class variations. Experimental results show that our approach has superior performance over the state-of-the- art methods, achieving a classification accuracy of 93.74 on our original emphysema database. Based on the classification results, we also perform the quantitative analysis of emphysema in 50 subjects by correlating the quantitative results (the area percentage of each class) with pulmonary functions. We show that centrilobular emphysema (CLE) and panlobular emphysema (PLE) have strong correlation with the pulmonary functions and the sum of CLE and PLE can be used as a new and accurate measure of emphysema severity instead of the conventional measure (sum of all subtypes of emphysema). The correlations between the new measure and various pulmonary functions are up to r 0.922 (r is correlation coefficient).
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