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A novel seeding method based on spatial sliding volume filter for neuron reconstruction

机译:一种基于空间滑动体积滤波的神经元重构新方法

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Automatic neuron reconstruction is one of the foremost challenging and important problem in the field of neuroscience. However, none of the prevalent algorithms can automatically reconstruct full anatomy structure. All of these make it is essential of developing new method for the tracing task. This paper introduced a novel seeding method for reconstructing neuron structures from 3-D microscopy images stacks. The protocol was initialized with a set of seeds which were detected by our proposed Sliding Volume Filter. And then the open curve snake was applied to the detected seeds to reconstruct the full structural of neuron cells. Results showed the proposed method exhibited excellent performance with its accuracy compared with traditional method. It is worth noting that the seeding method can clearly benefit for 3-D neuron fiber detection and reconstruction.
机译:自动神经元重建是神经科学领域的最重要挑战性和重要问题之一。但是,普遍的算法都不会自动重建完全解剖结构。所有这些都使得为追踪任务开发新方法。本文介绍了一种用于重建从3-D显微镜图像堆叠的神经元结构的新种播种方法。该方案用我们提出的滑动体积滤波器检测的一组种子初始化。然后将打开的曲线蛇施用于检测到的种子以重建神经元细胞的全结构。结果表明,与传统方法相比,该方法表现出优异的性能。值得注意的是,播种方法可以明显效益3-D神经元纤维检测和重建。

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