3D segmentations of neuronal nuclei from confocal microscope image stacks.
3D segmentations of neuronal nuclei from confocal microscope image stacks.
复制标题
DOI：
10.3389/fnana.2013.00049
复制
发表时间：
2013
影响因子：
2.9
通讯作者：
Defelipe J
中科院分区：
文献类型：
--
作者：
Latorre A;Alonso-Nanclares L;Muelas S;Peña JM;Defelipe J
在本文中，我们提出了一种算法来创建3D分割的神经元细胞从堆栈先前分割的2D图像。这个提议背后的想法是提供一种从2D堆栈重建3D结构的通用方法，而不管这些2D堆栈是如何获得的。该算法不仅重复使用在2D分割中获得的信息，而且还尝试纠正2D分割算法所犯的一些典型错误（例如，在紧密耦合的细胞簇的分割下）。我们已经测试了我们的算法在一个真实的神经元细胞核的分割在不同层的大鼠大脑皮层。几个有代表性的图像从不同层的大脑皮层已被认为是几个二维分割算法进行了比较。此外，该算法也被比较与传统的3D分水岭算法和这里得到的结果显示出更好的性能，在正确识别的神经细胞核。
In this paper, we present an algorithm to create 3D segmentations of neuronal cells from stacks of previously segmented 2D images. The idea behind this proposal is to provide a general method to reconstruct 3D structures from 2D stacks, regardless of how these 2D stacks have been obtained. The algorithm not only reuses the information obtained in the 2D segmentation, but also attempts to correct some typical mistakes made by the 2D segmentation algorithms (for example, under segmentation of tightly-coupled clusters of cells). We have tested our algorithm in a real scenario—the segmentation of the neuronal nuclei in different layers of the rat cerebral cortex. Several representative images from different layers of the cerebral cortex have been considered and several 2D segmentation algorithms have been compared. Furthermore, the algorithm has also been compared with the traditional 3D Watershed algorithm and the results obtained here show better performance in terms of correctly identified neuronal nuclei.
登录
查看更多内容
DOI：
10.1046/j.1365-2818.2000.00653.x
发表时间：
2000-03-01
期刊：
JOURNAL OF MICROSCOPY-OXFORD
影响因子：
--
作者：
Wu, HS;Barba, J;Gil, J
通讯作者：
Gil, J
影响因子：
3.7
作者：
Lin, G;Adiga, U;Roysam, B
通讯作者：
Roysam, B
影响因子：
10.6
作者：
Li, Chunming;Xu, Chenyang;Fox, Martin D.
通讯作者：
Fox, Martin D.
影响因子：
8
作者：
Bai, Xiangzhi;Sun, Changming;Zhou, Fugen
通讯作者：
Zhou, Fugen
影响因子：
4.4
作者：
MEYER, F
通讯作者：
MEYER, F