MedImages.jl

A comprehensive Julia library for GPU-accelerated, differentiable medical image processing.

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Table of Contents

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The Challenge

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The Solution

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Architecture Overview

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MedImage Data Structure

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Type Enumerations

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File I/O Operations

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Spatial Coordinate System

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Orientation Codes

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Basic Transformations

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Interpolation Methods

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Resampling Operations

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Cross-Modal Registration

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GPU Backend

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GPU Usage

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Differentiability

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Gradient Computation

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Complete Pipeline

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API Quick Reference

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Quick Start with Docker

The easiest way to get started is using Docker with GPU support for benchmarks.

Prerequisites

• Docker with NVIDIA GPU support (for GPU benchmarks)
• Or Docker without GPU (CPU-only mode available)

Build and Run

# Build the Docker image
make build

# Start interactive Julia REPL (with GPU)
make shell

# Start interactive Julia REPL (CPU only)
make shell-cpu

Run Tests

# Run the full test suite
make test

# Run tests in CPU-only mode
make test-cpu

Run Benchmarks

# Run GPU benchmarks (uses synthetic data)
make benchmark

# Run CPU-only benchmarks
make benchmark-cpu

# Custom benchmark options
make benchmark-custom ARGS="--size 64 --iterations 5"

Verify Setup

# Check CUDA/GPU availability
make check-cuda

# Check Python/SimpleITK setup
make check-python

# Run quick start verification
./scripts/quick-start.sh

Test Data

Test data files are expected in test_data/:

• volume-0.nii.gz - Primary NIfTI test file
• synthethic_small.nii.gz - Synthetic test file
• ScalarVolume_0/ - DICOM test directory

# Check test data availability
./scripts/check-test-data.sh

# Download benchmark data from TCIA
make download-data

# Convert DICOM to NIfTI for benchmarks
make convert-data

Note: Benchmarks use synthetic data by default (make benchmark). Real data download is only needed for make benchmark-full.

All Make Commands

make help  # Show all available commands

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Contributing

Contributions are welcome! If you have expertise in medical imaging, particularly ultrasonography, or experience with the technical challenges described above, please consider getting involved.

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References

[1] Gorgolewski, K.J., Auer, T., Calhoun, V.D. et al. The brain imaging data structure, a format for organizing and describing outputs of neuroimaging experiments. Sci Data 3, 160044 (2016). https://www.nature.com/articles/sdata201644