Brain Imaging Analysis Tutorial

This tutorial demonstrates a complete pipeline for analyzing brain imaging data using BrainShapeToolKit for shape analysis and DiffAM for appearance modeling.

Overview

Estimated time: 45 minutes
Difficulty: Intermediate
Prerequisites: Python 3.9+, Basic understanding of neuroimaging data

What You’ll Learn

• Load and preprocess brain MRI scans
• Extract shape features using BrainShapeToolKit
• Apply differentiable appearance modeling with DiffAM
• Visualize and interpret results

Dataset

This tutorial uses a sample dataset from the ADNI dataset. You can download a subset for testing:

# Download sample dataset
wget https://example.com/sstdv-docs/sample-brain-mri.zip
unzip sample-brain-mri.zip -d ./data/

Step 1: Setup Environment

# Clone the repositories
git clone https://github.com/SSTDV-Project/BrainShapeToolKit.git
git clone https://github.com/SSTDV-Project/DiffAM.git

# Install dependencies
cd BrainShapeToolKit
pip install -e .
cd ../DiffAM
pip install -e .

Step 2: Load and Preprocess Data

import nibabel as nib
import numpy as np
from brainshapetoolkit import MeshProcessor

# Load MRI scan
mri = nib.load('data/sample_brain.nii.gz')
mri_data = mri.get_fdata()

# Initialize mesh processor
processor = MeshProcessor()
mesh = processor.extract_surface(mri_data, threshold=0.5)
mesh_smooth = processor.smooth(mesh, iterations=10)

Step 3: Extract Shape Features

from brainshapetoolkit.features import ShapeFeatures

feature_extractor = ShapeFeatures()
features = feature_extractor.compute(mesh_smooth)

print(f"Number of features: {len(features)}")
print(f"Surface area: {features['surface_area']:.2f} mm²")
print(f"Volume: {features['volume']:.2f} mm³")

Step 4: Appearance Modeling with DiffAM

import diffam

# Initialize DiffAM model
model = diffam.DiffAM(pretrained=True)

# Extract appearance embeddings
embeddings = model.extract_embeddings(mesh_smooth)

# Generate synthetic variations
synthetic = model.interpolate(embeddings, n_samples=5)

Step 5: Visualization

import matplotlib.pyplot as plt

fig, axes = plt.subplots(1, 3, figsize=(15, 5))

axes[0].imshow(mri_data[:, :, mri_data.shape[2]//2], cmap='gray')
axes[0].set_title('Original MRI')

axes[1].imshow(mesh_smooth.render())
axes[1].set_title('Processed Surface')

axes[2].imshow(synthetic[0].render())
axes[2].set_title('Synthetic Variation')

plt.tight_layout()
plt.savefig('brain_imaging_results.png', dpi=150)

Expected Results

After completing this tutorial, you should have:

• Processed brain surface meshes ready for statistical analysis
• Extracted shape features for downstream analysis
• Synthetic appearance variations for data augmentation

Next Steps

• Breast Cancer Detection Tutorial - Apply similar techniques to medical imaging
• DiffAM Documentation - Deep dive into appearance modeling
• BrainShapeToolKit Reference - Complete API reference

Citation

If you use this pipeline in your research, please cite:

@article{sstdv2024brain,
  title={Brain Imaging Analysis Pipeline with Differentiable Appearance Modeling},
  author={SSTDV-Project},
  year={2024}
}