Project title
Augmented serial histology: towards a better understanding of 3D tissue microstructure
Summary
In recent decades, advances in imaging technology have helped the scientific community to link brain function to brain structure. Approaches such as magnetic resonance imaging (MRI) and positron emission tomography (PET), among others, have revolutionized our understanding of the brain. These tools have been used to study, for example, cerebral metabolism and neurodegenerative diseases. Despite their widespread use in neuroscience, these techniques are not well suited to studying brain structure on a micrometric scale.
In the field of neurophotonics, one tool that meets this growing demand for finer resolution is Serial Blockface Histology (SBH)1 . This technology combines a tissue slicer with a light microscope. The sample is cut into successive thin slices to reveal new layers of tissue to be imaged under the microscope. The process is repeated until the entire sample has been imaged. Then, using advanced image processing methods, the thousands of images acquired are assembled into a single 3D volume. SBH has been an essential imaging technique for many pioneering projects, such as mapping genomic expression in the mouse brain or imaging the connectome in mice.
One of the main challenges is that serial histology generates a considerable amount of data for each acquisition. For example, acquiring an entire mouse brain with a 40X objective offering a sampling resolution of 1 μm would require an estimated acquisition time of 60 days and would require around 700 terabytes of disk space to store the raw dataset. This represents a major challenge for data management, reconstruction and analysis methods. Future applications in this field of neurophotonics will increasingly require a close synergy between imaging and machine learning.
This research program has two objectives:
- Accelerate image acquisition and reconstruction by integrating the microscope with advanced computer vision methods, and
- Analyze the large amount of data generated by such imaging systems using machine learning-based methods.
The projects proposed in this research program aim to improve serial histology by integrating new state-of-the-art computer vision methods, image processing and machine learning techniques. This will make this imaging technique faster, smarter and more reproducible. It will also pave the way for the democratization of serial histology, and is a necessary step towards more widespread adoption of this technology by the biomedical research and clinical communities.
Granting organization
- Natural Sciences and Engineering Research Council of Canada(NSERC) Discovery Grant
Publications
- Lefebvre J, Delafontaine-Martel P, Lesage F. A Review of Intrinsic Optical Imaging Serial Blockface Histology (ICI-SBH) for Whole Rodent Brain Imaging. Photonics. Published online June 11, 2019:66. doi: 10.3390/photonics6020066