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Crossover Structure Separation With Application to Neuron Tracing in Volumetric Images
IEEE Transactions on Instrumentation and Measurement ( IF 5.6 ) Pub Date : 2021-04-09 , DOI: 10.1109/tim.2021.3072119
Changhao Guo , Min Liu , Tongkun Guan , Weixun Chen , He Wen , Tieyong Zeng , Yaonan Wang

Morphology reconstruction of neurons from 3-D microscopic images is essential to neuroscience research. However, many reconstructions may contain errors and ambiguities because of the crossover neuronal fibers. In this article, an automatic algorithm is proposed for the detection and separation of crossover structures and is applied to neuron tracing for improving the neuron reconstruction results. First, a spherical-patches extraction (SPE)-Net is employed to detect the 3-D neuron crossover points and locate the crossover structures in neuron volumetric images. Second, a multiscale upgraded ray-shooting model (MSURS) is proposed to obtain robust results at different scales with high confidence and is employed to extract the crossover neuronal structure features. Then, a crossover structure separation (CSS) method is developed to eliminate the false connections of crossover structures and generate deformed separated neuronal fibers based on the extracted features to replace the original neurites signals. Experiments demonstrate that the SPE-Net for crossover point detection achieves average precision and recall rates of 73.89% and 79.66%, respectively, and demonstrate the proposed CSS method can improve 20.46% the performance of the reconstructions on average. The results confirm that the proposed method can effectively improve the neuron tracing results in volumetric images.

中文翻译:

交叉结构分离及其在体积图像中神经元追踪中的应用

从3D显微图像重建神经元的形态对神经科学研究至关重要。但是,由于神经纤维交叉,许多重建可能包含错误和歧义。本文提出了一种用于交叉结构检测和分离的自动算法,并将其应用于神经元示踪,以改善神经元的重建结果。首先,采用球形补丁提取(SPE)-Net来检测3-D神经元交叉点并在神经元体积图像中定位交叉结构。其次,提出了一种多尺度升级射线拍摄模型(MSURS),以高置信度获得不同尺度下的鲁棒结果,并用于提取交叉神经元结构特征。然后,开发了一种交叉结构分离(CSS)方法,以消除交叉结构的错误连接,并基于提取的特征来生成变形的分离神经元纤维,以替换原始的神经突信号。实验表明,用于交叉点检测的SPE-Net分别达到73.89%和79.66%的平均精度和召回率,并证明了所提出的CSS方法可以平均提高20.46%的重建性能。实验结果表明,该方法可以有效地改善容积图像中神经元的跟踪结果。实验表明,用于交叉点检测的SPE-Net分别达到73.89%和79.66%的平均精度和召回率,并证明了所提出的CSS方法可以平均提高20.46%的重建性能。实验结果表明,该方法可以有效地改善容积图像中神经元的跟踪结果。实验表明,用于交叉点检测的SPE-Net分别达到73.89%和79.66%的平均精度和召回率,并证明了所提出的CSS方法可以平均提高20.46%的重建性能。实验结果表明,该方法可以有效地改善容积图像中神经元的跟踪结果。
更新日期:2021-04-27
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