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Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales

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Abstract

To evaluate the application of machine learning for the detection of subpleural pulmonary lesions (SPLs) in ultrasound (US) scans, we propose a novel boundary-restored network (BRN) for automated SPL segmentation to avoid issues associated with manual SPL segmentation (subjectivity, manual segmentation errors, and high time consumption). In total, 1612 ultrasound slices from 255 patients in which SPLs were visually present were exported. The segmentation performance of the neural network based on the Dice similarity coefficient (DSC), Matthews correlation coefficient (MCC), Jaccard similarity metric (Jaccard), Average Symmetric Surface Distance (ASSD), and Maximum symmetric surface distance (MSSD) was assessed. Our dual-stage boundary-restored network (BRN) outperformed existing segmentation methods (U-Net and a fully convolutional network (FCN)) for the segmentation accuracy parameters including DSC (83.45 ± 16.60%), MCC (0.8330 ± 0.1626), Jaccard (0.7391 ± 0.1770), ASSD (5.68 ± 2.70 mm), and MSSD (15.61 ± 6.07 mm). It also outperformed the original BRN in terms of the DSC by almost 5%. Our results suggest that deep learning algorithms aid fully automated SPL segmentation in patients with SPLs. Further improvement of this technology might improve the specificity of lung cancer screening efforts and could lead to new applications of lung US imaging.

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Funding

This study received funding from 2018 Supporting Project of Medical Guidance (Chinese and Western Medicine) of Science and Technology Commission of Shanghai Municipality (18411966700), 2019 Technical Standard Project of Shanghai "Science and Technology Innovation Action Plan" of Science and Technology Commission of Shanghai Municipality (19DZ2203300), Clinical Research Foundation of ShangHai Pulmonary Hospital (fk1940), Shanghai Sailing Program (No. 19YF1439300 & 19YF1440000) , Medical-Engineering Funding of Shanghai Jiao Tong University (No. ZH2018QNA24&ZH2018QNA20),National Key R&D Program of China (No. 2016YFC0904800). The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.

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Author notes

  1. Y.X., Y.Z., and K.B. contributed equally to this manuscript and thus should be considered as co-first authors

Authors and Affiliations

  1. Department of Ophthalmology, Shanghai General Hospital, National Clinical Research Center for Eye Diseases, Shanghai Jiao Tong University School of Medicine, Shanghai Key Laboratory of Ocular Fundus Disease, Shanghai Engineering Center for Visual Science and Photomedicine, Shanghai, 200080, China

    Yupeng Xu

  2. Department of Ultrasound, Shanghai Pulmonary Hospital, Tongji University School of Medicine, Shanghai, 200433, China

    Yi Zhang, Lisha Xu, Mengjun Shen & Yin Wang

  3. Department of Pathology, Tongji Hospital, Tongji University School of Medicine, Shanghai, 200092, China

    Ke Bi

  4. School of Computer Science, The University of Sydney, Sydney, NSW, 2006, Australia

    Zhiyu Ning

  5. Trauma Center, Shanghai General Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200080, China

    Guoying Deng

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  1. Yupeng Xu
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Contributions

All authors contributed to the planning of the work described. Y.Z., K.B., L.X. and M.S. conducted the data collection. Y.X., Z.N., G.D., and Y.W. analyzed and interpreted the results. All authors critically revised the content and approved the final manuscript.

Corresponding authors

Correspondence to Guoying Deng or Yin Wang.

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The authors declare that there are no conflicts of interest related to this article. This research was not sponsored by any company. The authors have full control of the data.

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Xu, Y., Zhang, Y., Bi, K. et al. Boundary Restored Network for Subpleural Pulmonary Lesion Segmentation on Ultrasound Images at Local and Global Scales. J Digit Imaging 33, 1155–1166 (2020). https://doi.org/10.1007/s10278-020-00356-8

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  • DOI: https://doi.org/10.1007/s10278-020-00356-8

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