Abstract
Segmentation is a preliminary step towards the development of automated computer aided diagnosis system (CAD). The system accuracy and efficiency primarily depend on the accurate segmentation result. Effective lung field segmentation is major challenging task, especially in the presence of different types of interstitial lung diseases (ILD). At present, high resolution computed tomography (HRCT) is considered to be the best imaging modality to observe ILD patterns. The most common patterns based on their textural appearances are consolidation, emphysema, fibrosis, ground glass opacity (GGO), reticulation and micronodules. In this paper, automatic lung field segmentation of pathological lung has been done using U-Net based deep convolutional networks. Our proposed model has been evaluated on publicly available MedGIFT database. The segmentation result was evaluated in terms of the dice similarity coefficient (DSC). Finally, the experimental results obtained on 330 testing images of different patterns achieving 94% of average DSC.
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Acknowledgments
The research is supported by National Institute of Technology, Durgapur by providing the required scientific environment and resources. The research work is funded by Visvesvaraya Ph.D. scheme of DeitY (Department of Electronics & Information Technology), Govt. of India. The authors are grateful to Medical College Kolkata and EKO DIAGNOSTICS, Kolkata for providing valuable advice for our research work.
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Kumar, A. et al. (2018). Segmentation of Lung Field in HRCT Images Using U-Net Based Fully Convolutional Networks. In: Nixon, M., Mahmoodi, S., Zwiggelaar, R. (eds) Medical Image Understanding and Analysis. MIUA 2018. Communications in Computer and Information Science, vol 894. Springer, Cham. https://doi.org/10.1007/978-3-319-95921-4_10
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DOI: https://doi.org/10.1007/978-3-319-95921-4_10
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