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Extracting Lungs from CT Images via Deep Convolutional Neural Network Based Segmentation and Two-Pass Contour Refinement

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Abstract

Lung segmentation is a key step of thoracic computed tomography (CT) image processing, and it plays an important role in computer-aided pulmonary disease diagnostics. However, the presence of image noises, pathologies, vessels, individual anatomical varieties, and so on makes lung segmentation a complex task. In this paper, we present a fully automatic algorithm for segmenting lungs from thoracic CT images accurately. An input image is first spilt into a set of non-overlapping fixed-sized image patches, and a deep convolutional neural network model is constructed to extract initial lung regions by classifying image patches. Superpixel segmentation is then performed on the preprocessed thoracic CT image, and the lung contours are locally refined according to corresponding superpixel contours with our adjacent point statistics method. Segmented lung contours are further globally refined by an edge direction tracing technique for the inclusion of juxta-pleural lesions. Our algorithm is tested on a group of thoracic CT scans with interstitial lung diseases. Experiments show that our algorithm creates an average Dice similarity coefficient of 97.95% and Jaccard’s similarity index of 94.48%, with 2.8% average over-segmentation rate and 3.3% under-segmentation rate compared with manually segmented results. Meanwhile, it shows better performance compared with several feature-based machine learning methods and current methods on lung segmentation.

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Funding

The work in this paper was supported by grants from the National Natural Science Foundation of China (Grant Nos. 41631175, 61702068), the Key Project of Ministry of Education for the 13th 5-years Plan of National Education Science of China (Grant No. DCA170302), the Social Science Foundation of Jiangsu Province of China (Grant No. 15TQB005), the Priority Academic Program Development of Jiangsu Higher Education Institutions (Grant No. 1643320H111) and the Postgraduate Research & Practice Innovation Program of Jiangsu Province (Grand No. KYCX19_0733).

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  1. Institute of EduInfo Science and Engineering, Nanjing Normal University, No. 122, Ninghai Ave., Nanjing, 210097, People’s Republic of China

    Caixia Liu & Mingyong Pang

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  1. Caixia Liu
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  2. Mingyong Pang
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Correspondence to Mingyong Pang.

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Liu, C., Pang, M. Extracting Lungs from CT Images via Deep Convolutional Neural Network Based Segmentation and Two-Pass Contour Refinement. J Digit Imaging 33, 1465–1478 (2020). https://doi.org/10.1007/s10278-020-00388-0

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  • DOI: https://doi.org/10.1007/s10278-020-00388-0

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