Joël Lefebvre is an associate professor of computer science at UQAM specialized in imaging and computer vision. His research interests include optical microscopy, neurophotonics, augmented imaging, biomedical image analysis, and deep learning. He is a associate researcher at the Montreal Heart Institute.
Postdoctoral fellowship in biomedical image analysis
University of Oxford
PhD in biomedical engineering, 2018
Master of Applied Science (MASc) in biomedical engineering, 2014
Bachelor of Engineering (B.Eng.) in Engineering Physics, 2012
In recent years, multiple serial histology techniques were developed to enable whole rodent brain imaging in 3-D. The main driving forces behind the emergence of these imaging techniques were the genome-wide atlas of gene expression in the mouse brain, the pursuit of the mouse brain connectome, and the BigBrain project. These projects rely on the use of optical imaging to target neuronal structures with histological stains or fluorescent dyes that are either expressed by transgenic mice or injected at specific locations in the brain. Efforts to adapt the serial histology acquisition scheme to use intrinsic contrast imaging (ICI) were also put forward, thus leveraging the natural contrast of neuronal tissue. This review focuses on these efforts. First, the origin of optical contrast in brain tissue is discussed with emphasis on the various imaging modalities exploiting these contrast mechanisms. Serial blockface histology (SBH) systems using ICI modalities are then reported, followed by a review of some of their applications. These include validation studies and the creation of multimodal brain atlases at a micrometer resolution. The paper concludes with a perspective of future developments, calling for a consolidation of the SBH research and development efforts around the world. The goal would be to offer the neuroscience community a single standardized open-source SBH solution, including optical design, acquisition automation, reconstruction algorithms, and analysis pipelines.
An automated dual-resolution serial optical coherence tomography (2R-SOCT) scanner is developed. The serial histology system combines a low-resolution (25 μm∕voxel) 3× OCT with a high-resolution (1.5 μm∕voxel) 40× OCT to acquire whole mouse brains at low resolution and to target specific regions of interest (ROIs) at high resolution. The 40× ROIs positions are selected either manually by the microscope operator or using an automated ROI positioning selection algorithm. Additionally, a multimodal and multiresolution registration pipeline is developed in order to align the 2R-SOCT data onto diffusion MRI (dMRI) data acquired in the same ex vivo mouse brains prior to automated histology. Using this imaging system, 3 whole mouse brains are imaged, and 250 high-resolution 40× three-dimensional ROIs are acquired. The capability of this system to perform multimodal imaging studies is demonstrated by labeling the ROIs using a mouse brain atlas and by categorizing the ROIs based on their associated dMRI measures. This reveals a good correspondence of the tissue microstructure imaged by the high-resolution OCT with various dMRI measures such as fractional anisotropy, number of fiber orientations, apparent fiber density, orientation dispersion, and intracellular volume fraction