Abstract
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.
Highlights
The mapping of the brain and neural circuits is currently a major endeavor in neuroscience and has great potential for facilitating an understanding of fundamental and pathological brain processes (Alivisatos et al, 2012; Kandel et al, 2013; Zuo et al, 2014)
As for low-quality or highly deformed brain data, though the registration accuracy of our method was reduced, our method still quite obviously surpassed other methods (Figure 2). For such challenging data types, we developed an interactive graphic user interface (GUI) to readily permit manual correction of the visible inaccuracies in the annotation file, through finely tuning the selected corresponding points (Figure 1e)
For processing various incomplete brain datasets, which were challenging for registration-based methods while remaining very common in neuroscience research, we applied our deep neural network to rapidly infer segmentations
Summary
The mapping of the brain and neural circuits is currently a major endeavor in neuroscience and has great potential for facilitating an understanding of fundamental and pathological brain processes (Alivisatos et al, 2012; Kandel et al, 2013; Zuo et al, 2014). Large projects, including the Mouse Brain Architecture project (Bohland et al, 2009), the Allen Mouse Brain Connectivity Atlas (Oh et al, 2014), and the Mouse Connectome project, have mapped the mouse brain (Zingg et al, 2014) in terms of cell types, long-range connectivity patterns, and microcircuit connectivity In addition to these large-scale collaborative efforts, an increasing number of laboratories are developing independent, automated, or semi-automated frameworks for processing brain data obtained for specific projects (Furth et al, 2018; Ni et al, 2020; Niedworok et al, 2016; Renier et al, 2016; Wang et al, 2020a; Iqbal et al, 2019).
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