大规模图像中记忆和时间效率高的三维神经元形态跟踪

Heng Wang, Donghao Zhang, Yang Song, Siqi Liu, Rong Gao, Hanchuan Peng, Weidong (Tom) Cai
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引用次数: 8

摘要

神经元形态的三维重建对于解决神经科学中神经元相关问题至关重要,因为它是研究神经元系统连接和功能的关键技术。为了提高数字神经元重建的准确性,人们提出了许多方法。然而,处理大规模图像需要大量的计算机内存和计算时间,这对我们提出了新的挑战。为了解决这个问题,我们引入了一种新的记忆(和时间)高效图像跟踪(MEIT)框架。在Gold数据集上进行了评估,在大多数情况下,我们提出的方法与最先进的神经元跟踪方法相比,在需要更少的内存和时间的情况下实现了更好或更具竞争力的性能。
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Memory and Time Efficient 3D Neuron Morphology Tracing in Large-Scale Images
3D reconstruction of neuronal morphology is crucial to solving neuron-related problems in neuroscience as it is a key technique for investigating the connectivity and functionality of the neuron system. Many methods have been proposed to improve the accuracy of digital neuron reconstruction. However, the large amount of computer memory and computation time they require to process the large-scale images have posed a new challenge for us. To solve this problem, we introduce a novel Memory (and Time) Efficient Image Tracing (MEIT) framework. Evaluated on the Gold dataset, our proposed method achieves better or competitive performance compared to state-of-the-art neuron tracing methods in most cases while requiring less memory and time.
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