CUBIC-Cloud: An Integrative Computational Framework Towards Community-driven Whole-Mouse-Brain Mapping

ABSTRACTRecent advancements in tissue clearing technologies have offered unparalleled opportunities for researchers to explore the whole mouse brain at cellular resolution. With the expansion of this experimental technique, however, a scalable and easy-to-use computational tool is in demand to effectively analyze and integrate whole-brain mapping datasets. To that end, here we present CUBIC-Cloud, a cloud-based framework to quantify, visualize and integrate whole mouse brain data. CUBIC-Cloud is a fully automated system where users can upload their whole-brain data, run analysis and publish the results. We demonstrate the generality of CUBIC-Cloud by a variety of applications. First, we investigated brain-wide distribution of PV, Sst, ChAT, Th and Iba1 expressing cells. Second, Aβ plaque deposition in AD model mouse brains were quantified. Third, we reconstructed neuronal activity profile under LPS-induced inflammation by c-Fos immunostaining. Last, we show brain-wide connectivity mapping by pseudo-typed Rabies virus. Together, CUBIC-Cloud provides an integrative platform to advance scalable and collaborative whole-brain mapping.

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

10.1101/2020.06.30.181255 ◽

2020 ◽

Cited By ~ 1

Author(s):

Xuechun Wang ◽

Weilin Zeng ◽

Xiaodan Yang ◽

Chunyu Fang ◽

Yunyun Han ◽

...

Keyword(s):

Deep Learning ◽

Image Registration ◽

Mouse Brain ◽

Training Data ◽

Registration Accuracy ◽

Whole Brain ◽

Microscopy Data ◽

Brain Data ◽

Digital Frame ◽

Channel Image

AbstractWe 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.

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CUBIC-Cloud provides an integrative computational framework toward community-driven whole-mouse-brain mapping

Cell Reports Methods ◽

10.1016/j.crmeth.2021.100038 ◽

2021 ◽

Vol 1 (2) ◽

pp. 100038

Author(s):

Tomoyuki Mano ◽

Ken Murata ◽

Kazuhiro Kon ◽

Chika Shimizu ◽

Hiroaki Ono ◽

...

Keyword(s):

Mouse Brain ◽

Brain Mapping ◽

Computational Framework

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Bi-channel image registration and deep-learning segmentation (BIRDS) for efficient, versatile 3D mapping of mouse brain

eLife ◽

10.7554/elife.63455 ◽

2021 ◽

Vol 10 ◽

Author(s):

Xuechun Wang ◽

Weilin Zeng ◽

Xiaodan Yang ◽

Chunyu Fang ◽

Yunyun Han ◽

...

Keyword(s):

Deep Learning ◽

Image Registration ◽

Mouse Brain ◽

Brain Regions ◽

Training Data ◽

Registration Accuracy ◽

Whole Brain ◽

Brain Data ◽

Digital Frame ◽

Channel Image

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.

Download Full-text