Quantitative and scalable whole-brain neuroanatomical mapping, with cellular resolution and molecular specificity, poses significant technological challenges. Indeed, a high image quality must be preserved reliably across the entire specimen and not only in a few representative volumes. On the other hand, robust and automated image analysis methods must be appropriately scalable to teravoxel datasets. Here, we present an experimental pipeline, involving tissue clearing, high-resolution light-sheet microscopy, volume registration to atlas, and deep learning strategies for image analysis, allowing the reconstruction of 3D maps of selected cell types in the whole mouse brain. We employed RAPID autofocusing [Silvestri et al., submitted] to keep the system sharply in focus throughout the entire mouse brain, without reducing the microscope throughput. Images were spatially anchored to reference atlas using semi-automatic tools (xNII family, http://www.nesys.uio.no). Finally, we used novel high-throughput tools for image processing, including deep learning strategies [Frasconi et al., 2014] to localize single neurons with high accuracy. By applying our pipeline to transgenically-labeled samples, we can produce an atlas of spatial distribution of genetically-defined cell types. Besides being a valuable reference for neurobiologists, these datasets can be used to build realistic simulations of neuronal functioning, such as in the Human Brain Project.
Ludovico Silvestri, Antonino Paolo Di Giovanna, Giacomo Mazzamuto, Francesco Orsini, Irene Costantini, Jan Bjaalie, Paolo Frasconi, and Francesco Saverio Pavone, "Mapping the quantitative cytoarchitecture of the whole mouse brain by light-sheet microscopy and digital brain atlasing (Conference Presentation)," Proc. SPIE 10481, Neural Imaging and Sensing 2018, 104810H (Presented at SPIE BiOS: January 29, 2018; Published: 14 March 2018); https://doi.org/10.1117/12.2286616.5751479184001.
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