HistoNeRF: An Accessible and Intelligent Approach for Comprehensive 2D-to-3D Histological Assessment

Area of Science:

• Biomedical research and diagnostic pathology
• Digital pathology and computational imaging

Background:

• Conventional 2D histology limits understanding of complex 3D tissue architecture.
• Obscured spatial relationships in 2D sections hinder diagnostic accuracy and biological interpretation.

Purpose of the Study:

• To develop HistoNeRF, a method for reconstructing high-fidelity 3D tissue volumes from routine histological sections.
• To create an accessible and reproducible 3D visualization tool for histological analysis.

Main Methods:

• Adaptation of Neural Radiance Fields (NeRF) for 3D volumetric reconstruction from digitized histological sections.
• Development of a convolutional neural network for tissue segmentation and a GPU-accelerated interface for visualization.
• Containerization of a Python-based graphical application (HistoNeRF GUI) using Docker for simplified deployment.

Main Results:

• Achieved high-fidelity 3D reconstructions with SSIM of 0.92 and Dice similarity coefficient of 0.88.
• Enabled expert histologists to better visualize follicular structures, stromal compartments, and vascular elements.
• Successfully deployed a containerized GUI from Docker Hub for immediate, installation-free access to 3D reconstruction.

Conclusions:

• HistoNeRF overcomes 2D microscopy limitations, enhancing visualization, interpretability, and reproducibility of histological architecture.
• The HCI-guided interface supports scalability and adoption in digital pathology workflows.
• The framework offers accurate, interactive 3D reconstructions advancing diagnostic precision and biomedical research, with potential for extension to diverse datasets.