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Automatic Segmentation of Pathological Glomerular Basement Membrane in Transmission Electron Microscopy Images with Random Forest Stacks

机译:随机林叠层透射电子显微镜图像中病理肾小球基底膜的自动分割

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摘要

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 RFS1 by simultaneously sampling several images of different grayscale ranges in the train phase. Testing integration can derive a zoom-view RFS2 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.
机译:通过透射电子显微镜(TEM)的病理分类对于某些肾病的诊断至关重要,并且GBM中的肾小球基底膜(GBM)中的厚度的变化通常用作诊断标准。通过计算机化技术的GBM对TEM图像的自动分割可以提供有关肾小球超微结构病变的清晰信息的临床医生。 TEM图像上的GBM区域不仅是复杂和变化的形状,而且还具有低对比度和灰度的广泛分布。因此,提取图像特征并获得优异的分段结果是困难的。为了解决这个问题,我们介绍了一种随机森林(RF-)的机器学习方法,即RF堆栈(RFS),实现自动分割。具体而言,这项工作提出了一种两级集成的RF,比单级集成RF更复杂,以提高精度和泛化性能。综合策略包括培训集成和测试集成。培训集成可以通过同时采样列车阶段中同时采样不同灰度范围的多个图像来派生全视图RFS1。测试集成可以通过单独采样不同灰度范围的图像并将结果集成在测试阶段中来导出缩放视图RFS2。实验结果表明,所提出的RF可用于自动分段不同的形态和灰度水平的基底膜。对GBM厚度测量和存款识别的未来研究将基于这项工作。

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