NeuroViz: An Integrated Pipeline for Pseudo-Colorized 3D Brain MRI Visualization Using Multi-Channel Feature Decomposition and Self-Supervised Deep Learning

Abstract

Grayscale brain Magnetic Resonance Imaging (MRI) scans, while rich in anatomical detail, present significant challenges in visual interpretability, tissue discrimination, and interactive exploration. We present NeuroViz, a novel end-to-end web-based pipeline for transforming raw NIfTI brain MRI volumes into interactive, pseudo-colorized, anatomically-segmented 3D visualizations. Our approach introduces a 9-channel feature decomposition framework that decomposes grayscale MRI slices into tissue intensity bands, gradient-based edge features, and texture/depth descriptors, subsequently mapping them to perceptually uniform CIE LAB color space using anatomically-motivated weighted contributions. We further enhance colorization quality through a self-supervised U-Net architecture trained on deterministic pseudo ground truth, enabling spatially-coherent, deep learning-driven colorization without the need for manually annotated datasets. The 3D reconstruction pipeline employs clinical-grade multi-stage skull stripping, marching cubes surface extraction, and sparse Laplacian mesh smoothing with per-vertex color mapping, exported as glTF 2.0 binary (GLB) models.

Introduction

This paper presents NeuroViz, an advanced web-based platform for MRI brain visualization that transforms traditional grayscale MRI scans into anatomically meaningful color images and interactive 3D brain models. Conventional MRI images are difficult to interpret because they rely solely on grayscale intensity variations, requiring expert radiological knowledge. NeuroViz addresses this limitation through intelligent pseudo-colorization, 3D reconstruction, and browser-based visualization.

The system tackles three major challenges: anatomically meaningful colorization, high-fidelity 3D brain reconstruction, and accessible interactive visualization through standard web browsers. Its key innovation is a 9-channel feature decomposition framework that extracts tissue information, edge features, texture characteristics, and spatial context from MRI images. These features are mapped into the perceptually uniform CIE LAB color space to generate clinically meaningful color representations of brain tissues such as cerebrospinal fluid (CSF), grey matter, and white matter.

To improve colorization quality, the authors introduce a self-supervised U-Net deep learning model that learns from the deterministic colorization pipeline without requiring manually labeled datasets. This teacher-student approach enables spatially coherent and smooth colorization while eliminating the need for expensive annotation processes.

A robust clinical-grade skull stripping pipeline removes non-brain tissues using thresholding, anatomical cuts, morphological operations, connected component filtering, dilation, and hole filling. This preprocessing step significantly improves the accuracy of colorization and 3D reconstruction.

For 3D visualization, NeuroViz employs Marching Cubes for surface extraction and Laplacian smoothing to generate high-quality brain meshes. The resulting models are exported in GLB format for efficient web-based rendering. The system also generates interactive volumetric point clouds to reveal internal brain structures.

The frontend integrates Flask, Three.js, and Plotly to provide multiple visualization modes, including multi-planar slice viewing, interactive 3D brain rendering, feature-channel inspection, annotation tools, and segmentation dashboards. Users can interact with MRI data directly through a web browser without specialized software.

Conclusion

We have presented NeuroViz, an integrated system for transforming raw brain MRI volumes into interactive, pseudo-colorized, 3D web-based visualizations. Our primary contribution—the 9-channel feature decomposition framework with CIE LAB color space mapping—demonstrates that meaningful, anatomically-informed colorization can be achieved through principled signal processing, without supervised training data. The self-supervised U-Net extension shows that deep learning can further enhance this colorization by learning spatial coherence from the deterministic teacher.The complete pipeline—from NIfTI input through skull stripping, colorization, segmentation, and 3D reconstruction to browser-based interactive exploration—bridges the gap between specialized neuroimaging workstations and accessible web-based tools. By combining classical image processing, deep learning, and modern web technologies, NeuroViz makes rich brain MRI visualization available to clinicians, educators, researchers, and patients through any standard web browser.

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Copyright

Copyright © 2026 Shravya MS, Akshatha S Havanur, Manasa J, Akhil Joshi, Ajay R. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.