School of Optical and Electronic Information- Wuhan National Laboratory for Optoelectronics, Huazhong University of Science and Technology, Wuhan, China.
School of Basic Medicine, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
Elife. 2021 Jan 18;10:e63455. doi: 10.7554/eLife.63455.
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.
我们开发了一种名为双通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 流水线包括图像预处理、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能够适应来自不同显微镜平台的各种类型的全脑数据，显示出显著提高的配准精度。此外，由于该平台将配准与神经网络相结合，因此其相对于其他平台的改进功能在于，配准过程可以方便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难以配准的不完整/有缺陷的大脑数据。因此，我们的软件经过优化，可以实现跨模态、全脑数据集的基于配准的微小时间尺度分割，或者对各种感兴趣的脑区进行基于实时推理的图像分割。通过可以适应大多数计算环境的 Fiji 插件，可以轻松提交和实施任务。
