Yang Bin, Huang Jiajin, Wu Gaowei, Yang Jian
Faculty of Information Technology, Beijing University of Technology, Beijing, China.
Beijing International Collaboration Base on Brain Informatics and Wisdom Services, Beijing, China.
Brain Inform. 2021 Nov 5;8(1):25. doi: 10.1186/s40708-021-00146-0.
Quickly and accurately tracing neuronal morphologies in large-scale volumetric microscopy data is a very challenging task. Most automatic algorithms for tracing multi-neuron in a whole brain are designed under the Ultra-Tracer framework, which begins the tracing of a neuron from its soma and traces all signals via a block-by-block strategy. Some neuron image blocks are easy for tracing and their automatic reconstructions are very accurate, and some others are difficult and their automatic reconstructions are inaccurate or incomplete. The former are called low Tracing Difficulty Blocks (low-TDBs), while the latter are called high Tracing Difficulty Blocks (high-TDBs). We design a model named 3D-SSM to classify the tracing difficulty of 3D neuron image blocks, which is based on 3D Residual neural Network (3D-ResNet), Fully Connected Neural Network (FCNN) and Long Short-Term Memory network (LSTM). 3D-SSM contains three modules: Structure Feature Extraction (SFE), Sequence Information Extraction (SIE) and Model Fusion (MF). SFE utilizes a 3D-ResNet and a FCNN to extract two kinds of features in 3D image blocks and their corresponding automatic reconstruction blocks. SIE uses two LSTMs to learn sequence information hidden in 3D image blocks. MF adopts a concatenation operation and a FCNN to combine outputs from SIE. 3D-SSM can be used as a stop condition of an automatic tracing algorithm in the Ultra-Tracer framework. With its help, neuronal signals in low-TDBs can be traced by the automatic algorithm and in high-TDBs may be reconstructed by annotators. 12732 training samples and 5342 test samples are constructed on neuron images of a whole mouse brain. The 3D-SSM achieves classification accuracy rates 87.04% on the training set and 84.07% on the test set. Furthermore, the trained 3D-SSM is tested on samples from another whole mouse brain and its accuracy rate is 83.21%.
在大规模体视显微镜数据中快速准确地追踪神经元形态是一项极具挑战性的任务。大多数用于全脑多神经元追踪的自动算法都是在Ultra-Tracer框架下设计的，该框架从神经元的胞体开始追踪，并通过逐块策略追踪所有信号。一些神经元图像块易于追踪，其自动重建非常准确，而另一些则很困难，其自动重建不准确或不完整。前者被称为低追踪难度块（low-TDBs），而后者被称为高追踪难度块（high-TDBs）。我们设计了一个名为3D-SSM的模型来对3D神经元图像块的追踪难度进行分类，该模型基于3D残差神经网络（3D-ResNet）、全连接神经网络（FCNN）和长短期记忆网络（LSTM）。3D-SSM包含三个模块：结构特征提取（SFE）、序列信息提取（SIE）和模型融合（MF）。SFE利用3D-ResNet和FCNN在3D图像块及其相应的自动重建块中提取两种特征。SIE使用两个LSTM来学习隐藏在3D图像块中的序列信息。MF采用拼接操作和FCNN来组合SIE的输出。3D-SSM可以用作Ultra-Tracer框架中自动追踪算法的停止条件。在其帮助下，低TDB中的神经元信号可以通过自动算法进行追踪，而高TDB中的神经元信号可能由注释者进行重建。在整个小鼠脑的神经元图像上构建了12732个训练样本和5342个测试样本。3D-SSM在训练集上的分类准确率为87.04%，在测试集上为84.07%。此外，在来自另一个完整小鼠脑的样本上对训练好的3D-SSM进行测试，其准确率为83.21%。
