College of Information and Engineering, Shenyang University, Shenyang, Liaoning, China.
College of Mechanical Engineering, Shenyang University of Technology, Shenyang, Liaoning, China.
Technol Health Care. 2021;29(S1):385-398. doi: 10.3233/THC-218037.
At present, there are many methods for pathological lung segmentation. However, there are still two unresolved problems. (1) The search steps in traditional ASM is a least square optimization method, which is sensitive to outlier marker points, and it makes the profile update to the transition area in the middle of normal lung tissue and tumor rather than a true lung contour. (2) If the noise images exist in the training dataset, the corrected shape model cannot be constructed.
To solve the first problem, we proposed a new ASM algorithm. Firstly, we detected these outlier marker points by a distance method, and then the different searching functions to the abnormal and normal marker points are applied. To solve the second problem, robust principal component analysis (RPCA) of low rank theory can remove noise, so the proposed method combines RPCA instead of PCA with ASM to solve this problem. Low rank decompose for marker points matrix of training dataset and covariance matrix of PCA will be done before segmentation using ASM.
Using the proposed method to segment 122 lung images with juxta-pleural tumors of EMPIRE10 database, got the overlap rate with the gold standard as 94.5%. While the accuracy of ASM based on PCA is only 69.5%.
The results showed that when the noise sample is contained in the training sample set, a good segmentation result for the lungs with juxta-pleural tumors can be obtained by the ASM based on RPCA.
目前，有许多用于肺部病理分割的方法。但是，仍然存在两个未解决的问题。（1）传统的 ASM 中的搜索步骤是最小二乘优化方法，该方法对异常标记点敏感，并且使轮廓更新到正常肺组织和肿瘤的中间过渡区域，而不是真实的肺轮廓。（2）如果训练数据集存在噪声图像，则无法构建校正的形状模型。
为了解决第一个问题，我们提出了一种新的 ASM 算法。首先，我们通过距离方法检测这些异常标记点，然后对异常和正常标记点应用不同的搜索功能。为了解决第二个问题，低秩理论的稳健主成分分析（RPCA）可以去除噪声，因此该方法将 RPCA 而不是 PCA 与 ASM 结合使用来解决此问题。在使用 ASM 进行分割之前，将对训练数据集的标记点矩阵和 PCA 的协方差矩阵进行低秩分解。
使用所提出的方法对 EMPIRE10 数据库中 122 个胸膜旁肿瘤的肺部图像进行分割，与金标准的重叠率为 94.5%。而基于 PCA 的 ASM 的准确率仅为 69.5%。
结果表明，当噪声样本包含在训练样本集中时，基于 RPCA 的 ASM 可以获得胸膜旁肿瘤肺部的良好分割结果。
