3D actin network centerline extraction with multiple active contours.
3D actin network centerline extraction with multiple active contours.
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DOI：
10.1016/j.media.2013.10.015
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发表时间：
2014-02
影响因子：
10.9
通讯作者：
Huang, Xiaolei
中科院分区：
文献类型：
--
作者：
Xu, Ting;Vavylonis, Dimitrios;Huang, Xiaolei
荧光显微镜常用于研究由细胞骨架聚合物纤维如肌动蛋白丝和肌动蛋白索形成的二维和三维网络结构。虽然这些细胞骨架结构通常被稀释到足以允许单个细丝或它们的束成像，但这些图像的定量分析是具有挑战性的。为了便于对图像数据进行定量、可重复和客观的分析，我们提出了一种半自动化的方法来提取肌动蛋白网络并在3D中检索其拓扑结构。我们的方法使用多个拉伸开放活动轮廓（SOAC），自动初始化的图像强度脊，然后演变沿着中心线的细丝在网络中。SOAC可以合并，在连接处停止，并与其他人重新配置，以允许在细丝的连接处平滑交叉。该方法一般适用于低信噪比的曲线网络图像。我们证明了它的潜力，通过提取的合成网络图像的中心线，肌动蛋白网络在2D全内反射荧光显微镜图像，和3D肌动蛋白电缆活分裂酵母细胞的旋转盘共聚焦显微镜成像。使用合成图像的方法的定量评估表明，对于SNR高于5.0的图像，由我们的结果与地面真实值之间的距离测量的平均顶点误差为1体素，平均Hausdorff距离低于10体素。
Fluorescence microscopy is frequently used to study two and three dimensional network structures formed by cytoskeletal polymer fibers such as actin filaments and actin cables. While these cytoskeletal structures are often dilute enough to allow imaging of individual filaments or bundles of them, quantitative analysis of these images is challenging. To facilitate quantitative, reproducible and objective analysis of the image data, we propose a semi-automated method to extract actin networks and retrieve their topology in 3D. Our method uses multiple Stretching Open Active Contours (SOACs) that are automatically initialized at image intensity ridges and then evolve along the centerlines of filaments in the network. SOACs can merge, stop at junctions, and reconfigure with others to allow smooth crossing at junctions of filaments. The proposed approach is generally applicable to images of curvilinear networks with low SNR. We demonstrate its potential by extracting the centerlines of synthetic meshwork images, actin networks in 2D Total Internal Reflection Fluorescence Microscopy images, and 3D actin cable meshworks of live fission yeast cells imaged by spinning disk confocal microscopy. Quantitative evaluation of the method using synthetic images shows that for images with SNR above 5.0, the average vertex error measured by the distance between our result and ground truth is 1 voxel, and the average Hausdorff distance is below 10 voxels.
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影响因子：
2
作者：
Beil, M;Braxmeier, H;Walther, P
通讯作者：
Walther, P
影响因子：
3.7
作者：
Krauss P;Metzner C;Lange J;Lang N;Fabry B
通讯作者：
Fabry B
DOI：
10.1109/isbi.2009.5193303
发表时间：
2009-06-28
期刊：
Proceedings. IEEE International Symposium on Biomedical Imaging
影响因子：
--
作者：
Li H;Shen T;Smith MB;Fujiwara I;Vavylonis D;Huang X
通讯作者：
Huang X
DOI：
10.1109/34.368173
发表时间：
1995-02-01
影响因子：
23.6
作者：
MALLADI, R;SETHIAN, JA;VEMURI, BC
通讯作者：
VEMURI, BC
DOI：
10.1109/tpami.2007.70713
发表时间：
2008-03-01
影响因子：
23.6
作者：
Melonakos, John;Pichon, Eric;Tannenbaum, Allen
通讯作者：
Tannenbaum, Allen