Calligrapher, artist, biomedical engineer,
scientific visualization expert and passionate with in silico neuroscience.
Generation of high fidelity, two-manifold and watertight polygonal surface meshes of neuroscientific models including spiny neurons, glial cells and blood vasculature from high resolution segmented electron microscopy stacks. These polygonal meshes are used to create homogeneous tetrahedral or volumetric meshes for reaction-diffusion simulations, for example using the STEPs simulator, to understand the functional mechanisms of neuro-glio-vascular ensemble.
It is all about NeuroMorphoVis; an integrated, interactive, extensible and cross-platform framework for building, visualizing and analyzing digital reconstructions of neuronal morphology skeletons extracted from microscopy stacks. The framework is capable of detecting and repairing several tracing artifacts, allowing the generation of high fidelity surface meshes and high resolution volumetric models for simulation and in silico studies. NeuroMorphoVis is based on Blender 2.79 and freely available on Github.
Development of efficient and unbiased physically-plausible optical models for rendering fluorescent structures in low- and highly-scattering turbid media. These models can be further employed to simulate the optical pipelines of several fluorescence-based optical microscopes, such as the epi-widefield or the light-sheet fluorescence microscopes, to digitally reconstruct in silico fluorescence imaging experiments. The models can also be used to simulate other types of experiments such as voltage sensitive dyes imaging and Calcium imaging.