Marwan Abdellah's

Calligrapher, artist, biomedical engineer,
scientific visualization expert and passionate with in silico neuroscience.

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Research & Development

Mesh Reconstruction


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.

Neuronal Visualization


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, 2.8 and 2.9 and is freely available on Github.

Brain Vasculature


It is all about VessMorphoVis; an integrated suite of toolboxes for interactive visualization and analysis of vast brain vascular networks represented by morphological graphs segmented originally from imaging or microscopy stacks. The tool leverages the outstanding potentials of Blender, aiming to establish an integrated, extensible and domain-specific framework capable of interactive visualization, analysis, repair, high-fidelity meshing and high-quality rendering of vascular morphologies. VessMorphoVis is based on Blender 2.8 and 2.9 and is freely available on Github.

In Silico Imaging


Research and development of Monte Carlo-based optical models for rendering realistic visualizations of digitally-reconstrucred neocortical tissue models, allowing to simulate optical imaging experiments on a physically-plausible basis.

Fluorescence Rendering


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.

Parallel Rendering


High performance, distributed and parallel rendering (sort last and sort first) of large scale volumetric models of biological and medical imaging datasets on multi-CPU, multi-GPU and multi-node computing clusters using CUDA and OpenCL.

Biomedical Imaging


Interactive GPU-based (using graphics or compute shaders) visualization and processing of biomedical datasets acquired with various imaging modalities such as computed tomography, magnetic resonance imaging and also four-dimensional ultrasound scanners.

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