Binary surface smoothing for abnormal lung segmentation

Accurate lung segmentation in high-resolution computed tomography (HRCT) is important for lung disease diagnosis. When high attenuation patterns with challenging variations in intensity or shape exist in peripheral lung, the binary lung surface generated from coarse segmentation is often uneven, which makes lung segmentation inaccurate. This paper presents a novel surface smoothing method for abnormal lung segmentation, we employ a double-surfaced-based smoothing algorithm to smooth the binary lung surface, which can remove noise while filling holes in uneven surface. Besides, for abnormal lungs with different severity, our method can adaptively refine the uneven areas to achieve the accurate results of segmentation. Fifty-five lung HRCT scans with interstitial lung disease (ILD) are used to evaluate our proposed method, and the experimental results demonstrate that the proposed approach can improve the accuracy of abnormal lung segmentation significantly (overlap rate = 97.14%, Hausdorff Distance = 6.28 mm). (C) 2020 Elsevier Ltd. All rights reserved.