3D Reconstruction of Human Olfactory Projection

Authors: Victoria F Low, Chinchien Lin, Shan Su, Mahyar Osanlouy, Mona Khan, Soroush Safaei, Gonzalo Maso Talou, Maurice A Curtis, Peter Mombaerts
Publication: Communications Biology (November 2024)
Code: Zenodo Repository
Interactive 3D Model: Neuroglancer Platform

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Overview

This study presents a computational pipeline to reconstruct the 3D microanatomy of the human olfactory system, from the nasal cavity to the olfactory bulb—using fluorescence histology, deep learning, and high-performance computing (HPC). The workflow addresses challenges in processing terabyte-scale datasets and provides new insights into olfactory sensory neuron (OSN) distribution and axon trajectories.

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Key Computational Contributions

1. First end-to-end pipeline for large-scale 3D reconstruction of human olfactory tissues (~7.45 cm³ specimen, 1234 sections).
2. CNN-based segmentation achieving Dice scores >0.85 for critical structures (OSNs, glomeruli, vasculature).
3. HPC-optimized registration reducing banana-effect artifacts via multi-resolution deformable alignment.
4. Public 3D dataset enabling interactive exploration of olfactory projection via Neuroglancer.

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Methodology

Computational Pipeline

From fluorescence histology to 3D visualization – click to expand

1. Fluorescence Histology

   • Quadruple staining: Hoechst (nuclei), UEA1 (OSNs), OMP (mature OSNs), VGLUT2 (axon terminals).
   • Whole-slide scanning: 1.097 µm/pixel resolution, ~2.9 TB raw data.

2. CNN Segmentation

   • Architecture: Modified 2D U-Net with 4 input channels (Hoechst + 3 markers).
   • Training: Bootstrap approach with iterative ground truth expansion (20-45 sections/structure).
   • Key layers:

     # Simplified U-Net structure
     encoder = [Conv2D(8→16→32), MaxPooling2D]
     bottleneck = Conv2D(64) + Upsampling2D
     decoder = [Conv2D(32→16→8), Concatenate(skip connections)]
     

3. HPC Registration

   • Intra-block: Parallel registration of 247 blocks (5 sections each) using SimpleElastix.
   • Inter-block: Banana-effect correction via affine + B-spline transformations between blocks.
   • Metrics: Mutual information for intensity alignment, DSC for structural consistency.

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Results

Segmentation Performance

Structure	Dice Score	Binary Cross-Entropy
Vasculature	0.808	0.0148
OSNs	0.760	0.0173
Glomeruli	0.779	0.0017

OSN segmentation achieved single-cell resolution in sparse regions but grouped cells in dense zones.

Registration Efficiency

• 1082 CPU hours on 96 Intel Xeon Gold 6136 cores
• 16% error reduction vs. sequential registration
• Tolerance: ±80 µm axial drift corrected

Key Findings

1. OSN Count: ~2.7 million OSNs calculated via morphometric extrapolation (90% CI: 2.4–2.9M).
2. Fila Olfactoria: 34 foramina identified in cribriform plate (17/side).
3. Non-uniform Distribution: Olfactory epithelium showed serrated borders and posterior-anterior density gradient.

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Implications

Technical Advancements

• Scalable ML: Method enables processing of whole-brain datasets (~100x mouse brain volume).
• Clinical Potential: Pipeline adaptable for Parkinson’s/Alzheimer’s studies via α-synuclein/tau staining.
• Open Science: First public 3D olfactory dataset with ~5.8 GB/channel resolution.

Biological Insights

• Challenges mouse-to-human extrapolation: ~10x fewer OSNs/glomerulus vs. mice.
• Provides baseline for studying SARS-CoV-2 olfactory dysfunction.

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Computational Tools Used

• Segmentation: TensorFlow U-Net, Fiji for ground truth
• Registration: SimpleElastix, ITK
• Visualization: ParaView, Neuroglancer
• HPC: New Zealand eScience Infrastructure (NeSI)

Interactive 3D exploration

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