Cao Lei, Lu YanMeng, Li ChuangQuan, Yang Wei
School of Biomedical Engineering, Southern Medical University, GuangZhou 510515, China.
Central Laboratory, Southern Medical University, GuangZhou 510515, China.
Comput Math Methods Med. 2019 Mar 25;2019:1684218. doi: 10.1155/2019/1684218. eCollection 2019.
Pathological classification through transmission electron microscopy (TEM) is essential for the diagnosis of certain nephropathy, and the changes of thickness in glomerular basement membrane (GBM) and presence of immune complex deposits in GBM are often used as diagnostic criteria. The automatic segmentation of the GBM on TEM images by computerized technology can provide clinicians with clear information about glomerular ultrastructural lesions. The GBM region on the TEM image is not only complicated and changeable in shape but also has a low contrast and wide distribution of grayscale. Consequently, extracting image features and obtaining excellent segmentation results are difficult. To address this problem, we introduce a random forest- (RF-) based machine learning method, namely, RF stacks (RFS), to realize automatic segmentation. Specifically, this work proposes a two-level integrated RFS that is more complicated than a one-level integrated RF to improve accuracy and generalization performance. The integrated strategies include training integration and testing integration. Training integration can derive a full-view RFS by simultaneously sampling several images of different grayscale ranges in the train phase. Testing integration can derive a zoom-view RFS by separately sampling the images of different grayscale ranges and integrating the results in the test phase. Experimental results illustrate that the proposed RFS can be used to automatically segment different morphologies and gray-level basement membranes. Future study on GBM thickness measurement and deposit identification will be based on this work.
通过透射电子显微镜（TEM）进行病理分类对于某些肾病的诊断至关重要，肾小球基底膜（GBM）厚度的变化以及GBM中免疫复合物沉积的存在常被用作诊断标准。利用计算机技术对TEM图像上的GBM进行自动分割，可以为临床医生提供有关肾小球超微结构病变的清晰信息。TEM图像上的GBM区域不仅形状复杂多变，而且对比度低、灰度分布广。因此，提取图像特征并获得良好的分割结果具有一定难度。为了解决这个问题，我们引入一种基于随机森林（RF）的机器学习方法，即随机森林堆叠（RFS），来实现自动分割。具体而言，这项工作提出了一种两级集成RFS，它比一级集成RF更复杂，以提高准确性和泛化性能。集成策略包括训练集成和测试集成。训练集成可以通过在训练阶段同时对几个不同灰度范围的图像进行采样来获得全视图RFS。测试集成可以通过在测试阶段分别对不同灰度范围的图像进行采样并整合结果来获得缩放视图RFS。实验结果表明，所提出的RFS可用于自动分割不同形态和灰度级的基底膜。未来关于GBM厚度测量和沉积物识别的研究将基于这项工作。
