Cell Structure and Mechanobiology Group, Department of Biomedical Engineering, The University of Melbourne, Australia.
Cell Structure and Mechanobiology Group, Department of Biomedical Engineering, The University of Melbourne, Australia; Systems Biology Laboratory, Melbourne School of Engineering, University of Melbourne, Australia.
J Struct Biol. 2018 Jun;202(3):275-285. doi: 10.1016/j.jsb.2018.02.005. Epub 2018 Feb 22.
This paper presents a new algorithm to automatically segment the myofibrils, mitochondria and nuclei within single adult cardiac cells that are part of a large serial-block-face scanning electron microscopy (SBF-SEM) dataset. The algorithm only requires a set of manually drawn contours that roughly demarcate the cell boundary at routine slice intervals (every 50th, for example). The algorithm correctly classified pixels within the single cell with 97% accuracy when compared to manual segmentations. One entire cell and the partial volumes of two cells were segmented. Analysis of segmentations within these cells showed that myofibrils and mitochondria occupied 47.5% and 51.6% on average respectively, while the nuclei occupy 0.7% of the cell for which the entire volume was captured in the SBF-SEM dataset. Mitochondria clustering increased at the periphery of the nucleus region and branching points of the cardiac cell. The segmentations also showed high area fraction of mitochondria (up to 70% of the 2D image slice) in the sub-sarcolemmal region, whilst it was closer to 50% in the intermyofibrillar space. We finally demonstrate that our segmentations can be turned into 3D finite element meshes for cardiac cell computational physiology studies. We offer our large dataset and MATLAB implementation of the algorithm for research use at www.github.com/CellSMB/sbfsem-cardiac-cell-segmenter/. We anticipate that this timely tool will be of use to cardiac computational and experimental physiologists alike who study cardiac ultrastructure and its role in heart function.
本文提出了一种新的算法，用于自动分割成年心肌细胞中的肌原纤维、线粒体和细胞核，这些细胞是大型连续块面扫描电子显微镜 (SBF-SEM) 数据集的一部分。该算法仅需要一组手动绘制的轮廓，这些轮廓大致勾勒出常规切片间隔（例如，每隔 50 个切片）的细胞边界。与手动分割相比，该算法对单个细胞内的像素进行了 97%的正确分类。分割了一个完整的细胞和两个细胞的部分体积。对这些细胞内的分割进行分析表明，肌原纤维和线粒体分别平均占据 47.5%和 51.6%，而细胞核占据整个细胞的 0.7%，而整个细胞的体积都被捕获在 SBF-SEM 数据集中。线粒体在细胞核区域的周边和心脏细胞的分支点处聚集。分割还显示出亚肌节区域中线粒体的高面积分数（高达二维图像切片的 70%），而在肌原纤维间空间中则更接近 50%。最后，我们证明我们的分割可以转换为 3D 有限元网格，用于心脏细胞计算生理学研究。我们提供了我们的大型数据集和算法的 MATLAB 实现，供在 www.github.com/CellSMB/sbfsem-cardiac-cell-segmenter/ 上进行研究使用。我们预计这个及时的工具将对研究心脏超微结构及其在心脏功能中的作用的心脏计算和实验生理学家都有用。
