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Bi-channel Image Registration and Deep-learning Segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

By Xuechun Wang, Weilin Zeng, Xiaodan Yang, Chunyu Fang, Yunyun Han, Peng Fei

Posted 02 Jul 2020
bioRxiv DOI: 10.1101/2020.06.30.181255

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 of mouse brain. BIRDS features a graphical user interface that is used to submit jobs, monitor their progress, and display results. It implements a full pipeline including image pre-processing, bi-channel registration, automatic annotation, creation of 3D digital frame, high-resolution visualization, and expandable quantitative analysis (via link with Imaris). The new bi-channel registration algorithm is adaptive to various types of whole brain data from different microscopy platforms and shows obviously improved registration accuracy. Also, the attraction of combing registration with neural network lies in that the registration procedure can readily provide training data for network, while the network can efficiently segment incomplete/defective brain data that are otherwise difficult for registration. 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 for various brain region of interests. Jobs can be easily implemented on Fiji plugin that can be adapted for most computing environments. ### Competing Interest Statement The authors have declared no competing interest.

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