Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain.
Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain.
复制标题
双通道图像配准和深度学习分割 (BIRDS)，可实现高效、多功能的小鼠大脑 3D 映射
作者：
Wang X;Zeng W;Yang X;Zhang Y;Fang C;Zeng S;Han Y;Fei P
我们开发了一种名为双通道图像配准和深度学习分割（BIRDS）的开源软件，用于3D显微镜数据的映射和分析，并将其应用于小鼠大脑。BIRDS管道包括图像预处理、双通道配准、自动注释、创建3D数字帧、高分辨率可视化和可扩展的定量分析。这种新的双通道配准算法适用于来自不同显微镜平台的各种类型的全脑数据，并显着提高了配准精度。此外，由于该平台将配准与神经网络相结合，其相对于其他平台的改进功能在于配准过程可以容易地提供用于网络构建的训练数据，而经训练的神经网络可以有效地分割否则难以配准的不完整/有缺陷的大脑数据。因此，我们的软件进行了优化，以实现跨模态，全脑数据集的基于分钟时标配准的分割或各种感兴趣的大脑区域的基于实时推理的图像分割。作业可以通过Fiji插件轻松提交和实现，该插件可以适应大多数计算环境。绘制小鼠大脑中的所有细胞和神经连接是神经科学界的一个主要目标，因为这将为大脑如何工作以及疾病期间发生的事情提供新的见解。为了实现这一目标，研究人员必须首先捕获大脑的三维图像。然后使用计算工具处理这些图像，这些工具可以识别大脑中不同的解剖特征和细胞类型。各种显微镜技术被用来捕捉大脑的三维图像。这导致越来越多的计算程序可以从这些图像中提取数据。然而，这些工具是专门为某些显微镜技术设计的。例如，有些工作在全脑数据集上，而另一些则用于分析特定的大脑区域。因此，开发一种更灵活、标准化的方法来注释大脑的显微镜图像，将使研究人员能够更有效地分析数据，并比较不同实验的结果。为此，Wang，Zeng，Yang等人设计了一个开源软件程序，用于从使用不同显微镜捕获的三维大脑图像中提取特征。与其他工具类似，该程序使用“图像配准”方法，能够识别和注释大脑中的特征。然而，这些工具仅限于全脑数据集，其中每个特征的完整解剖结构必须存在，以便被软件识别。为了克服这一点，Wang等人将图像配准方法与深度学习算法相结合，该算法使用图像中的像素来识别大脑孤立区域的特征。虽然这些神经网络不需要全脑图像，但它们确实需要大型数据集来“学习”。因此，图像配准方法也有利于神经网络，因为它提供了一个算法可以训练的注释特征数据集。Wang等人表明，他们的名为BIRDS的软件程序可以准确地识别大脑区域成像数据集中的像素级大脑特征，以及全脑图像。深度学习算法还可以适应分析来自不同显微镜平台的各种类型的成像数据。这个开源软件应该可以让研究人员更容易地分享、分析和比较来自不同实验的大脑成像数据集。
We have developed an open-source software called bi-channel image registration and deep-learning segmentation (BIRDS) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image preprocessing, bi-channel registration, automatic annotation, creation of a 3D digital frame, high-resolution visualization, and expandable quantitative analysis. This new bi-channel registration algorithm is adaptive to various types of whole-brain data from different microscopy platforms and shows dramatically improved registration accuracy. Additionally, as this platform combines registration with neural networks, its improved function relative to the other platforms lies in the fact that the registration procedure can readily provide training data for network construction, while the trained neural network can efficiently segment-incomplete/defective brain data that is otherwise difficult to register. Our software is thus optimized to enable either minute-timescale registration-based segmentation of cross-modality, whole-brain datasets or real-time inference-based image segmentation of various brain regions of interest. Jobs can be easily submitted and implemented via a Fiji plugin that can be adapted to most computing environments. Mapping all the cells and nerve connections in the mouse brain is a major goal of the neuroscience community, as this will provide new insights into how the brain works and what happens during disease. To achieve this, researchers must first capture three-dimensional images of the brain. These images are then processed using computational tools that can identify distinct anatomical features and cell types within the brain. Various microscopy techniques are used to capture three-dimensional images of the brain. This has led to an increasing number of computational programs that can extract data from these images. However, these tools have been specifically designed for certain microscopy techniques. For example, some work on whole-brain datasets while others are built to analyze specific brain regions. Developing a more flexible, standardized method for annotating microscopy images of the brain would therefore enable researchers to analyze data more efficiently and compare results across experiments. To this end, Wang, Zeng, Yang et al. have designed an open-source software program for extracting features from three-dimensional brain images which have been captured using different microscopes. Similar to other tools, the program uses an ‘image registration’ method that is able to recognize and annotate features in the brain. These tools, however, are limited to whole-brain datasets in which the complete anatomy of each feature must be present in order to be recognized by the software. To overcome this, Wang et al. combined the image registration method with a deep-learning algorithm which uses pixels in the image to identify features in isolated regions of the brain. Although these neural networks do not require whole-brain images, they do need large datasets to ‘learn’ from. Therefore, the image registration method also benefits the neural network by providing a dataset of annotated features that the algorithm can train on. Wang et al. showed that their software program, named BIRDS, could accurately recognize pixel-level brain features within imaging datasets of brain regions, as well as whole-brain images. The deep-learning algorithm could also adapt to analyze various types of imaging data from different microscopy platforms. This open-source software should make it easier for researchers to share, analyze and compare brain imaging datasets from different experiments.