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PRF-RW: a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation

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Abstract

The computational detection of lung lobes from computed tomography images is a challenging segmentation problem with important respiratory healthcare applications, including emphysema, chronic bronchitis, and asthma. This paper proposes a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation. First, our model performs automated segmentation of the lung lobes in a progressive random forest network, eliminating the need for prior segmentation of lungs, vessels, or airways. Then, an interactive lobes segmentation approach based on random walk mechanism is designed for improving auto-segmentation accuracy. Furthermore, we annotate a new dataset which contains 93 scans (57 men, 36 women; age range: 40–90 years) from the Central Hospital Affiliated with Shenyang Medical College (CHASMC). We evaluate the model on our annotated dataset, LIDC (https://wiki.cancerimagingarchive.net) and LOLA11 (http://lolall.com/) datasets. The proposed model achieved a Dice score of \(0.906 \pm 0.106\) for LIDC, \(0.898 \pm 0.113\) for LOLA11, and \(0.921 \pm 0.101\) for our dataset. Experimental results show the accuracy of the proposed approach, which consistently improves performance across different datasets by a maximum of 8.2% as compared to baselines model.

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Acknowledgements

The authors acknowledge support for the research reported in this paper through the research development fund at the Project of National Key R&D Program of China (2018YFC1311900) and the Project of National Key Technology R&D Program of the Ministry of Science and Technology (2017YFC0114200). The authors sincerely thank Prof. Shuyue Xia at the Central Hospital Affiliated to Shenyang Medical College for providing image data.

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Authors and Affiliations

  1. Northeastern University, Shenyang, Liaoning, China

    Qiang Li, Xiangju Li & Yan Kang

  2. Neusoft Medical Systems Ltd., Shenyang, Liaoning, China

    Qiang Li & Lei Chen

  3. Neusoft Corporation, Shenyang, Liaoning, China

    Xiaofeng Lv

  4. The Central Hospital Affiliated to Shenyang Medical College, Shenyang, Liaoning, China

    Shuyue Xia

  5. Shenzhen Technology University, Shenzhen, China

    Yan Kang

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  1. Qiang Li
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Correspondence to Yan Kang.

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Li, Q., Chen, L., Li, X. et al. PRF-RW: a progressive random forest-based random walk approach for interactive semi-automated pulmonary lobes segmentation. Int. J. Mach. Learn. & Cyber. 11, 2221–2235 (2020). https://doi.org/10.1007/s13042-020-01111-9

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