A general approach for automatic segmentation of pneumonia, pulmonary nodule, and tuberculosis in CT images

被引:4
作者
Wang, Lu [1 ,2 ]
Zhou, He [2 ]
Xu, Nan [2 ]
Liu, Yuchan [3 ]
Jiang, Xiran [4 ]
Li, Shu [2 ]
Feng, Chaolu [5 ]
Xu, Hainan [6 ]
Deng, Kexue [3 ]
Song, Jiangdian [2 ]
机构
[1] China Med Univ, Shengjing Hosp, Dept Lib, Shenyang 110004, Liaoning, Peoples R China
[2] China Med Univ, Sch Hlth Management, Shenyang 110122, Liaoning, Peoples R China
[3] Univ Sci & Technol China USTC, USTC, Dept Radiol, Div Life Sci & Med,Affiliated Hosp 1, Hefei 230036, Anhui, Peoples R China
[4] China Med Univ, Sch Intelligent Med, Shenyang 110122, Liaoning, Peoples R China
[5] Minist Educ, Key Lab Intelligent Comp Med Image MIC, Shenyang 110169, Liaoning, Peoples R China
[6] China Med Univ, Pelv Floor Dis Diag & Treatment Ctr, Dept Obstet & Gynecol, Shengjing Hosp, Shenyang 110004, Liaoning, Peoples R China
基金
中国国家自然科学基金;
关键词
LUNG NODULES; CLASSIFICATION; ATTENTION;
D O I
10.1016/j.isci.2023.107005
中图分类号
O [数理科学和化学]; P [天文学、地球科学]; Q [生物科学]; N [自然科学总论];
学科分类号
07 ; 0710 ; 09 ;
摘要
Proposing a general segmentation approach for lung lesions, including pulmonary nodules, pneumonia, and tuberculosis, in CT images will improve efficiency in radiology. However, the performance of generative adversarial networks is hampered by the limited availability of annotated samples and the catastrophic forgetting of the discriminator, whereas the universality of traditional morphology-based methods is insufficient for segmenting diverse lung lesions. A cascaded dual-attention network with a context-aware pyramid feature extraction module was designed to address these challenges. A self-supervised rotation loss was designed to mitigate discriminator forgetting. The proposed model achieved Dice coefficients of 70.92, 73.55, and 68.52% on multi-center pneumonia, lung nodule, and tuberculosis test datasets, respectively. No significant decrease in accuracy was observed (p > 0.10) when a small training sample size was used. The cyclic training of the discriminator was reduced with self -supervised rotation loss (p < 0.01). The proposed approach is promising for segmenting multiple lung lesion types in CT images.
引用
收藏
页数:19
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