Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality
Large-scale Exploration of Neuronal Morphologies Using Deep Learning and Augmented Reality
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DOI：
10.1007/s12021-018-9361-5
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发表时间：
2018-10-01
期刊：
影响因子：
3
通讯作者：
Zhang, Shaoting
中科院分区：
文献类型：
--
作者：
Li, Zhongyu;Butler, Erik;Zhang, Shaoting
最近公布的大规模神经元形态学数据极大地促进了神经信息学的研究。然而，这些数据的庞大数量和复杂性对有效和准确的神经元探索提出了重大挑战。在本文中，我们提出了一个有效的检索框架来解决这些问题，基于深度学习和二进制编码的前沿技术。我们首次为神经元形态数据开发了一种基于深度学习的特征表示方法，其中3D神经元首先被投影到二值图像中，然后使用无监督的深度神经网络学习特征，即，堆叠卷积自动编码器（SCAE）。深特征随后与手工制作的特征融合以获得更准确的表示。考虑到在大规模数据库中穷举搜索通常非常耗时，我们采用了一种新的二进制编码方法将特征向量压缩成简短的二进制代码。我们的框架在包括58，000个神经元的公共数据集上进行了验证，与最先进的方法相比，显示出有希望的检索精度和效率。此外，我们开发了一种新的神经元可视化程序的基础上增强现实（AR）技术，它可以帮助用户采取深入的探索神经元形态的交互式和沉浸式的方式。
Recently released large-scale neuron morphological data has greatly facilitated the research in neuroinformatics. However, the sheer volume and complexity of these data pose significant challenges for efficient and accurate neuron exploration. In this paper, we propose an effective retrieval framework to address these problems, based on frontier techniques of deep learning and binary coding. For the first time, we develop a deep learning based feature representation method for the neuron morphological data, where the 3D neurons are first projected into binary images and then learned features using an unsupervised deep neural network, i.e., stacked convolutional autoencoders (SCAEs). The deep features are subsequently fused with the hand-crafted features for more accurate representation. Considering the exhaustive search is usually very time-consuming in large-scale databases, we employ a novel binary coding method to compress feature vectors into short binary codes. Our framework is validated on a public data set including 58,000 neurons, showing promising retrieval precision and efficiency compared with state-of-the-art methods. In addition, we develop a novel neuron visualization program based on the techniques of augmented reality (AR), which can help users take a deep exploration of neuron morphologies in an interactive and immersive manner.