We have developed an open-source software called BIRDS (bi-channel image registration and deep-learning segmentation) for the mapping and analysis of 3D microscopy data and applied this to the mouse brain. The BIRDS pipeline includes image pre-processing, 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 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.

中文翻译：

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双通道图像配准和深度学习分割 (BIRDS) 用于高效、多功能的小鼠大脑 3D 映射

我们开发了一种名为 BIRDS（双通道图像配准和深度学习分割）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 数字框架、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，并显示出显着提高的配准精度。此外，由于该平台将注册与神经网络相结合，其相对于其他平台的改进功能在于注册过程可以很容易地为网络构建提供训练数据，而经过训练的神经网络可以有效地分割不完整/有缺陷的大脑数据，否则这些数据很难注册。因此，我们的软件经过优化，可以对跨模态、全脑数据集进行基于分钟时间尺度配准的分割，或者对各种感兴趣的大脑区域进行基于实时推理的图像分割。可以通过适用于大多数计算环境的 Fiji 插件轻松提交和实施作业。