How to extract overlapping patches from a 3D volume and recreate the input shape from the patches?

Extracting Overlapping Patches from a 3D Volume

Introduction

This article explains how to extract overlapping patches from a 3D volume and subsequently reconstruct the original volume from these extracted patches. This process is crucial for various tasks in fields like computer vision, medical imaging, and scientific data analysis.

Why Use Overlapping Patches?

• Efficient Memory Management: Processing the entire 3D volume at once might be memory-intensive, especially for large datasets. Patch extraction allows processing in smaller chunks.
• Parallel Processing: Overlapping patches can be processed independently, making the process suitable for parallelization and speeding up computation.
• Contextual Information: Overlapping patches provide surrounding information for each extracted patch, enabling more accurate analysis or processing.

Extracting Patches

Here’s a conceptual breakdown of the patch extraction process:

Code Example (Python)

import numpy as np

def extract_patches(volume, patch_size, stride):
    patches = []
    for z in range(0, volume.shape[0] - patch_size[0] + 1, stride[0]):
        for y in range(0, volume.shape[1] - patch_size[1] + 1, stride[1]):
            for x in range(0, volume.shape[2] - patch_size[2] + 1, stride[2]):
                patch = volume[z:z+patch_size[0], y:y+patch_size[1], x:x+patch_size[2]]
                patches.append(patch)
    return patches

# Example usage
volume = np.random.rand(100, 100, 100) # Sample 3D volume
patch_size = (10, 10, 10)
stride = (5, 5, 5)
extracted_patches = extract_patches(volume, patch_size, stride)

Reconstructing the Volume from Patches

Averaging

A simple reconstruction method involves averaging the overlapping values of patches. Each voxel in the reconstructed volume is the average of the corresponding values from all patches that contribute to that voxel.

Code Example (Python)

def reconstruct_volume(patches, original_shape, patch_size, stride):
    reconstructed_volume = np.zeros(original_shape)
    count_volume = np.zeros(original_shape)
    for i, patch in enumerate(patches):
        z, y, x = i * stride
        reconstructed_volume[z:z+patch_size[0], y:y+patch_size[1], x:x+patch_size[2]] += patch
        count_volume[z:z+patch_size[0], y:y+patch_size[1], x:x+patch_size[2]] += 1
    reconstructed_volume = reconstructed_volume / count_volume
    return reconstructed_volume

# Example usage
reconstructed_volume = reconstruct_volume(extracted_patches, volume.shape, patch_size, stride)

Other Reconstruction Methods

• Weighted Averaging: Assign weights to patches based on their distance from the center of the voxel being reconstructed.
• Patch-Based Interpolation: Use interpolation techniques to fill in gaps between patches.
• Deep Learning: Train a neural network to reconstruct the volume from the extracted patches.

Conclusion

Extracting and reconstructing overlapping patches from 3D volumes offers a flexible approach for processing large datasets. By understanding the principles behind patch extraction and reconstruction, you can implement efficient and accurate methods for various tasks, including data analysis, visualization, and machine learning.