BACKGROUND With the advent of new high-throughput electron microscopy techniques such as serial block-face scanning electron microscopy (SBF-SEM) and focused ion-beam scanning electron microscopy (FIB-SEM) biomedical scientists can study sub-cellular structural mechanisms of heart disease at high resolution and high volume. Among several key components that determine healthy contractile function in cardiomyocytes are Z-disks or Z-lines, which are located at the lateral borders of the sarcomere, the fundamental unit of striated muscle. Z-disks play the important role of anchoring contractile proteins within the cell that make the heartbeat. Changes to their organization can affect the force with which the cardiomyocyte contracts and may also affect signaling pathways that regulate cardiomyocyte health and function. Compared to other components in the cell, such as mitochondria, Z-disks appear as very thin linear structures in microscopy data with limited difference in contrast to the remaining components of the cell. METHODS In this paper, we propose to generate a 3D model of Z-disks within single adult cardiac cells from an automated segmentation of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The proposed fully automated segmentation scheme is comprised of three main modules including "pre-processing", "segmentation" and "refinement". We represent a simple, yet effective model to perform segmentation and refinement steps. Contrast stretching, and Gaussian kernels are used to pre-process the dataset, and well-known "Sobel operators" are used in the segmentation module. RESULTS We have validated our model by comparing segmentation results with ground-truth annotated Z-disks in terms of pixel-wise accuracy. The results show that our model correctly detects Z-disks with 90.56% accuracy. We also compare and contrast the accuracy of the proposed algorithm in segmenting a FIB-SEM dataset against the accuracy of segmentations from a machine learning program called Ilastik and discuss the advantages and disadvantages that these two approaches have. CONCLUSIONS Our validation results demonstrate the robustness and reliability of our algorithm and model both in terms of validation metrics and in terms of a comparison with a 3D visualisation of Z-disks obtained using immunofluorescence based confocal imaging.

中文翻译：

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从高通量扫描电子显微镜数据自动分割心肌Z盘。

背景技术随着新的高通量电子显微镜技术的出现，例如串行块面扫描电子显微镜（SBF-SEM）和聚焦离子束扫描电子显微镜（FIB-SEM），生物医学科学家可以研究心脏的亚细胞结构机制高分辨率和高产量的疾病。决定心肌细胞健康收缩功能的几个关键因素是Z盘或Z线，它们位于横纹肌的基本单位即肌小节的外侧边界。Z盘起着将收缩蛋白锚定在构成心跳的细胞内的重要作用。其组织的变化可以影响心肌细胞收缩的力，还可能影响调节心肌细胞健康和功能的信号传导途径。与细胞中的其他成分（例如线粒体）相比，Z盘在显微镜数据中显示为非常细的线性结构，与细胞的其余成分相比，差异有限。方法在本文中，我们建议从大型串行块面扫描电子显微镜（SBF-SEM）数据集的自动分割中，生成单个成年心脏细胞内Z盘的3D模型。提议的全自动分割方案由三个主要模块组成，包括“预处理”，“分割”和“优化”。我们代表一个简单但有效的模型来执行细分和优化步骤。使用对比度拉伸和高斯核对数据集进行预处理，在细分模块中使用众所周知的“ Sobel运算符”。结果我们通过将分割结果与带有真实注释的Z盘在像素方向上的准确性进行比较来验证了我们的模型。结果表明，我们的模型正确检测Z盘的准确度为90.56％。我们还比较和对比了该算法在分割FIB-SEM数据集时的准确性与来自名为Ilastik的机器学习程序进行分割的准确性，并讨论了这两种方法的优缺点。结论我们的验证结果证明了我们的算法和模型的鲁棒性和可靠性，既包括验证指标，也包括与使用基于免疫荧光共聚焦成像的Z盘的3D可视化进行比较的结果。