Augmented serial blockface histology: Toward a better understanding of 3D tissue microstructure

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Over the last decades, technological advances in imaging assisted the scientific community for linking functions to structures inside the brain. Neuroimaging modalities such as magnetic resonance imaging (MRI) and positron emission tomography (PET) have revolutionized our understanding of the brain. These tools have been used to study, among other investigations, brain metabolism and neurodegenerative diseases. Despite the ubiquitous use of MRI and PET imaging in neuroscience, these techniques are not well suited when it comes to study the brain structure at a micrometer scale. In the field of neurophotonics, a tool that meets this increasing resolution requirement is serial blockface histology (SBH). This technology combines a tissue slicing apparatus with an optical microscope. The sample is sequentially sliced to reveal new tissue layers that are imaged with the microscope. The process is repeated until the whole sample has been imaged. Then, through advanced registration methods, the thousands of image tiles acquired are assembled into a single 3D volume. SBH was an essential component of many high-profile neuroscience projects mapping genome-wide gene expression and for obtaining a micrometre scale connectome in a whole mouse brain. One of the main challenges is that SBH generates a tremendous amount of data for every brain. For example, to acquire an entire mouse brain with a 40X objective offering sampling resolution of 1 µm would require an estimated acquisition time of 60 days with current SBH systems and would necessitate around 700 Terabytes (TB) of disk space to store the raw dataset. This represents a challenge for data management, reconstruction, and the analysis methods. Future applications in this neurophotonics field will increasingly require a closer synergy between imaging and machine learning. My Discovery research program has two objectives: (1) to accelerate image acquisition and reconstruction by integrating the microscope with advanced computer vision methods, and (2) to analyze the large amount of data generated by such imaging systems with machine learning based methods. The projects proposed in this research program aim to create an augmented serial blockface histology system by integrating the microscopy with novel computer vision methods, image processing and machine learning techniques. This will make this imaging technique faster, smarter and more reproducible. This will open the way to democratization of SBH and is a necessary step toward the broader adoption of this technology by the biomedical research and clinical communities.