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Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs

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Abstract

Ground-glass opacity (GGO) is a common CT imaging sign on high-resolution CT, which means the lesion is more likely to be malignant compared to common solid lung nodules. The automatic recognition of GGO CT imaging signs is of great importance for early diagnosis and possible cure of lung cancers. The present GGO recognition methods employ traditional low-level features and system performance improves slowly. Considering the high-performance of CNN model in computer vision field, we proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling is performed on multi-views and multi-receptive fields, which reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has the ability to obtain the optimal fine-tuning model. Multi-CNN models fusion strategy obtains better performance than any single trained model. We evaluated our method on the GGO nodule samples in publicly available LIDC-IDRI dataset of chest CT scans. The experimental results show that our method yields excellent results with 96.64% sensitivity, 71.43% specificity, and 0.83 F1 score. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.

We proposed an automatic recognition method of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNN models in this paper. Our hybrid resampling reduces the risk of missing small or large GGOs by adopting representative sampling panels and processing GGOs with multiple scales simultaneously. The layer-wise fine-tuning strategy has ability to obtain the optimal fine-tuning model. Our method is a promising approach to apply deep learning method to computer-aided analysis of specific CT imaging signs with insufficient labeled images.

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Funding

The study has been supported by National Natural Science Foundation of China (60973059 and 81171407), Program for New Century Excellent Talents in University (NCET-10-0044).

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Authors and Affiliations

  1. Beijing Lab of Intelligent Information, School of Computer Science, Beijing Institute of Technology, 5 South Zhongguancun Street, Haidian District, Beijing, 100081, China

    Guanghui Han, Xiabi Liu, Guangyuan Zheng, Murong Wang & Shan Huang

  2. College of Information Science and Engineering, Henan University of Technology, 100 Lianhua Street, Gaoxin District, Zhengzhou, 450001, China

    Guanghui Han

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  1. Guanghui Han
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  2. Xiabi Liu
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  3. Guangyuan Zheng
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Correspondence to Guanghui Han.

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Han, G., Liu, X., Zheng, G. et al. Automatic recognition of 3D GGO CT imaging signs through the fusion of hybrid resampling and layer-wise fine-tuning CNNs. Med Biol Eng Comput 56, 2201–2212 (2018). https://doi.org/10.1007/s11517-018-1850-z

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  • DOI: https://doi.org/10.1007/s11517-018-1850-z

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