Abstract 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).

中文翻译：

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用于异常肺分割的二元表面平滑

摘要 高分辨率计算机断层扫描 (HRCT) 中准确的肺分割对于肺病诊断很重要。当周围肺中存在强度或形状具有挑战性变化的高衰减模式时，粗分割产生的二元肺表面通常不均匀，这使得肺分割不准确。本文提出了一种新的异常肺分割的表面平滑方法，我们采用基于双曲面的平滑算法来平滑二值肺表面，它可以在填充不平整表面的孔的同时去除噪声。此外，对于不同严重程度的异常肺，我们的方法可以自适应地细化不均匀区域，以达到准确的分割结果。55 例间质性肺病 (ILD) 的肺 HRCT 扫描用于评估我们提出的方法，