PIXER: an automated particle-selection method based on segmentation using a deep neural network
PIXER: an automated particle-selection method based on segmentation using a deep neural network
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PIXER：一种基于深度神经网络分割的自动粒子选择方法
DOI：
10.1186/s12859-019-2614-y
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发表时间：
2019-01-18
影响因子：
3
通讯作者：
Zhang, Fa
中科院分区：
文献类型：
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作者：
Zhang, Jingrong;Wang, Zihao;Zhang, Fa
背景低温电子显微镜（cryo-electron microscopy，cryo-EM）已成为测定蛋白质和大分子复合物结构的一种广泛使用的工具。为了获得单粒子冷冻EM重建的输入，研究人员必须从显微照片中选择数十万个粒子。由于显微图像的信噪比极低，目前的自动颗粒分选方法的性能还不能满足研究要求。为了将研究人员从这项繁重的工作中解放出来，并获得大量高质量的粒子，我们提出了一种基于深度神经网络分割思想的自动粒子选择方法（PIXER）。首先，为了适应低信噪比条件，我们使用分割网络将显微照片转换为概率密度图。这些概率密度图表明显微照片的每个像素是粒子的一部分而不仅仅是背景噪声的可能性。从密度图中选择的颗粒比直接从原始噪声显微照片中选择的颗粒具有更鲁棒的信号。其次，目前还没有冷冻EM的分割训练数据集。为了实现我们的计划，我们提出了一种自动化的方法来生成一个训练数据集，用于使用真实世界的数据进行分割。第三，我们提出了一种基于网格的局部最大值方法来定位粒子的概率密度图。我们在模拟和真实世界的实验数据集上测试了我们的方法，并将PIXER与主流方法RELION，DeepEM和DeepPicker进行了比较，以证明其性能。结果表明，作为一个完全自动化的方法，PIXER可以获得的结果一样好的半自动化方法RELION和DeepEM.ConclusionTo我们所知，我们的工作是第一个解决粒子选择问题，使用分割网络的概念。作为一种全自动的粒子选择方法，PIXER可以将研究人员从繁重的粒子选择工作中解放出来。根据实验结果，PIXER可以在低信噪比条件下在几分钟内获得准确的结果。
BackgroundCryo-electron microscopy (cryo-EM) has become a widely used tool for determining the structures of proteins and macromolecular complexes. To acquire the input for single-particle cryo-EM reconstruction, researchers must select hundreds of thousands of particles from micrographs. As the signal-to-noise ratio (SNR) of micrographs is extremely low, the performance of automated particle-selection methods is still unable to meet research requirements. To free researchers from this laborious work and to acquire a large number of high-quality particles, we propose an automated particle-selection method (PIXER) based on the idea of segmentation using a deep neural network.ResultsFirst, to accommodate low-SNR conditions, we convert micrographs into probability density maps using a segmentation network. These probability density maps indicate the likelihood that each pixel of a micrograph is part of a particle instead of just background noise. Particles selected from density maps have a more robust signal than do those directly selected from the original noisy micrographs. Second, at present, there is no segmentation-training dataset for cryo-EM. To enable our plan, we present an automated method to generate a training dataset for segmentation using real-world data. Third, we propose a grid-based, local-maximum method to locate the particles from the probability density maps. We tested our method on simulated and real-world experimental datasets and compared PIXER with the mainstream methods RELION, DeepEM and DeepPicker to demonstrate its performance. The results indicate that, as a fully automated method, PIXER can acquire results as good as the semi-automated methods RELION and DeepEM.ConclusionTo our knowledge, our work is the first to address the particle-selection problem using the segmentation network concept. As a fully automated particle-selection method, PIXER can free researchers from laborious particle-selection work. Based on the results of experiments, PIXER can acquire accurate results under low-SNR conditions within minutes.