A workflow for the automatic segmentation of organelles in electron microscopy image stacks.
A workflow for the automatic segmentation of organelles in electron microscopy image stacks.
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DOI：
10.3389/fnana.2014.00126
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发表时间：
2014
影响因子：
2.9
通讯作者：
Ellisman MH
中科院分区：
文献类型：
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作者：
Perez AJ;Seyedhosseini M;Deerinck TJ;Bushong EA;Panda S;Tasdizen T;Ellisman MH
电子显微镜（EM）有助于分析各种病理过程中关键细胞器系统的形式、分布和功能状态，包括与神经退行性疾病相关的病理过程。此类EM数据通常为潜在疾病机制提供重要的新见解。发展更准确、更有效的方法来量化亚细胞显微解剖学的变化，已经被证明是理解帕金森病、阿尔茨海默病以及青光眼发病机制的关键。虽然我们获取大量3D EM数据的能力正在迅速发展，但需要更先进的分析工具来帮助测量数据集中的细胞器的精确三维形态，这些数据集可能包括数百到数千个完整的细胞。尽管新的成像仪器每天的数据吞吐量可以超过三像素，但图像分割和分析仍然是实现整个细胞结构细胞器定量描述的重要瓶颈。在此，我们提出了一种新的3D EM图像堆中细胞器的自动分割方法。仅使用二维图像信息生成分割，使该方法适用于各向异性成像技术，如连续块面扫描电子显微镜（SBEM）。此外，没有关于三维细胞器形态的假设，确保该方法可以很容易地扩展到任何数量的结构和功能不同的细胞器。在介绍了我们的算法之后，我们通过在一个示例SBEM数据集中评估不同细胞器目标的分割精度来验证其性能，并证明它可以在超级计算资源上有效地并行化，从而大大减少了运行时间。
Electron microscopy (EM) facilitates analysis of the form, distribution, and functional status of key organelle systems in various pathological processes, including those associated with neurodegenerative disease. Such EM data often provide important new insights into the underlying disease mechanisms. The development of more accurate and efficient methods to quantify changes in subcellular microanatomy has already proven key to understanding the pathogenesis of Parkinson's and Alzheimer's diseases, as well as glaucoma. While our ability to acquire large volumes of 3D EM data is progressing rapidly, more advanced analysis tools are needed to assist in measuring precise three-dimensional morphologies of organelles within data sets that can include hundreds to thousands of whole cells. Although new imaging instrument throughputs can exceed teravoxels of data per day, image segmentation and analysis remain significant bottlenecks to achieving quantitative descriptions of whole cell structural organellomes. Here, we present a novel method for the automatic segmentation of organelles in 3D EM image stacks. Segmentations are generated using only 2D image information, making the method suitable for anisotropic imaging techniques such as serial block-face scanning electron microscopy (SBEM). Additionally, no assumptions about 3D organelle morphology are made, ensuring the method can be easily expanded to any number of structurally and functionally diverse organelles. Following the presentation of our algorithm, we validate its performance by assessing the segmentation accuracy of different organelle targets in an example SBEM dataset and demonstrate that it can be efficiently parallelized on supercomputing resources, resulting in a dramatic reduction in runtime.
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DOI：
10.1109/tbme.2011.2168396
发表时间：
2012-01
期刊：
IEEE transactions on bio-medical engineering
影响因子：
--
作者：
Jaume S;Knobe K;Newton RR;Schlimbach F;Blower M;Reid RC
通讯作者：
Reid RC
影响因子：
10.6
作者：
Chan, TF;Vese, LA
通讯作者：
Vese, LA
影响因子：
64.8
作者：
Bock, Davi D.;Lee, Wei-Chung Allen;Kerlin, Aaron M.;Andermann, Mark L.;Hood, Greg;Wetzel, Arthur W.;Yurgenson, Sergey;Soucy, Edward R.;Kim, Hyon Suk;Reid, R. Clay
通讯作者：
Reid, R. Clay
影响因子：
5.8
作者：
Giuly, Richard J.;Kim, Keun-Young;Ellisman, Mark H.
通讯作者：
Ellisman, Mark H.
影响因子：
64.8
作者：
通讯作者：
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