medrs

High-performance medical imaging I/O and processing library for Rust and Python.

Overview

medrs is designed for throughput-critical medical imaging workflows, particularly deep learning pipelines that process large 3D volumes. It provides:

• Fast NIfTI I/O: Memory-mapped reading, crop-first loading (read sub-volumes without loading entire files)
• Transform Pipeline: Lazy evaluation with automatic operation fusion and SIMD acceleration
• Mixed Precision: Native f16/bf16 support for 50% storage savings
• Random Augmentation: Reproducible, GPU-friendly augmentations for ML training
• Python Bindings: Zero-copy numpy views, direct PyTorch/JAX tensor creation
• MONAI Integration: Drop-in replacements for MONAI transforms

Why medrs?

Performance vs MONAI & TorchIO (128³ volume)

At larger volumes (512³), speedups increase dramatically: up to 38,000x vs MONAI and 6,600x vs TorchIO.

Storage Efficiency (128³ volume, compressed)

Key Advantages

1. Crop-First Loading: Load 64³ patch from 512³ volume without reading entire file - 6,600x faster than MONAI
2. Mixed Precision: Save in bf16/f16 for 40-50% smaller files with minimal precision loss
3. MONAI Drop-in: Replace MONAI I/O transforms with one import change
4. Zero-Copy: Direct tensor creation without intermediate numpy allocations

Comprehensive Benchmark Results

Benchmark results comparing medrs, MONAI, and TorchIO across multiple volume sizes and operations.

Load Performance (Basic I/O)

Crop-First Loading (64³ patch)

Load Resampled (Half resolution)

Direct PyTorch Loading

Load with Z-Normalization

Benchmarks run on Apple M1 Pro, 20 iterations, 3 warmup. Run your own: python benchmarks/bench_medrs.py

Installation

Python

pip install medrs

Rust

[dependencies]
medrs = "0.1"

Development

git clone https://github.com/liamchalcroft/med-rs.git
cd med-rs
pip install -e ".[dev]"
maturin develop --features python

Quick Start

Python:

import medrs
import torch

# Load a NIfTI image
img = medrs.load("brain.nii.gz")
print(f"Shape: {img.shape}, Spacing: {img.spacing}")

# Method chaining for transforms
processed = img.resample([1.0, 1.0, 1.0]).z_normalize().clamp(-1, 1)
processed.save("output.nii.gz")

# Load directly to PyTorch tensor (most efficient)
tensor = medrs.load_to_torch("brain.nii.gz", dtype=torch.float16, device="cuda")

Rust:

use medrs::nifti;
use medrs::transforms::{resample_to_spacing, Interpolation};

fn main() -> medrs::Result<()> {
    let img = nifti::load("brain.nii.gz")?;
    println!("Shape: {:?}, Spacing: {:?}", img.shape(), img.spacing());

    let resampled = resample_to_spacing(&img, [1.0, 1.0, 1.0], Interpolation::Trilinear)?;
    nifti::save(&resampled, "output.nii.gz")?;
    Ok(())
}

Transform Pipeline

Build composable transform pipelines with lazy evaluation and automatic optimization:

Python:

import medrs

# Create a reusable pipeline
pipeline = medrs.TransformPipeline()
pipeline.z_normalize()
pipeline.clamp(-1.0, 1.0)
pipeline.resample_to_shape([64, 64, 64])

# Apply to multiple images
for path in image_paths:
    img = medrs.load(path)
    processed = pipeline.apply(img)

Rust:

use medrs::pipeline::compose::TransformPipeline;

let pipeline = TransformPipeline::new()
    .z_normalize()
    .clamp(-1.0, 1.0)
    .resample_to_shape([64, 64, 64]);

let processed = pipeline.apply(&img);

Random Augmentation

Reproducible augmentations for ML training with optional seeding:

Python:

import medrs

img = medrs.load("brain.nii.gz")

# Individual augmentations
flipped = medrs.random_flip(img, axes=[0, 1, 2], prob=0.5, seed=42)
noisy = medrs.random_gaussian_noise(img, std=0.1, seed=42)
scaled = medrs.random_intensity_scale(img, scale_range=0.1, seed=42)
shifted = medrs.random_intensity_shift(img, shift_range=0.1, seed=42)
rotated = medrs.random_rotate_90(img, axes=(0, 1), seed=42)
gamma = medrs.random_gamma(img, gamma_range=(0.7, 1.5), seed=42)

# Combined augmentation (flip + noise + scale + shift)
augmented = medrs.random_augment(img, seed=42)

Rust:

use medrs::transforms::{random_flip, random_gaussian_noise, random_augment};

// Individual augmentations
let flipped = random_flip(&img, &[0, 1, 2], Some(0.5), Some(42))?;
let noisy = random_gaussian_noise(&img, Some(0.1), Some(42))?;

// Combined augmentation
let augmented = random_augment(&img, Some(42))?;

Crop-First Loading

Load only the data you need - essential for training pipelines:

import medrs
import torch

# Load a 64^3 patch starting at position (32, 32, 32)
patch = medrs.load_cropped("volume.nii", [32, 32, 32], [64, 64, 64])

# Load with resampling and reorientation in one step
patch = medrs.load_resampled(
    "volume.nii",
    output_shape=[64, 64, 64],
    target_spacing=[1.0, 1.0, 1.0],
    target_orientation="RAS"
)

# Load directly to GPU tensor
tensor = medrs.load_cropped_to_torch(
    "volume.nii",
    output_shape=[64, 64, 64],
    target_spacing=[1.0, 1.0, 1.0],
    dtype=torch.float16,
    device="cuda"
)

Training Data Loader

High-performance patch extraction for training:

import medrs

loader = medrs.TrainingDataLoader(
    volumes=["vol1.nii", "vol2.nii", "vol3.nii"],
    patch_size=[64, 64, 64],
    patches_per_volume=4,
    patch_overlap=[0, 0, 0],
    randomize=True,
    cache_size=1000
)

for patch in loader:
    # Training loop
    tensor = patch.to_torch()

Available Transforms

Intensity Transforms

• z_normalize() / z_normalization() - Zero mean, unit variance
• rescale() / rescale_intensity() - Scale to [min, max] range
• clamp() - Clamp values to range

Spatial Transforms

• resample() / resample_to_spacing() - Resample to target spacing
• resample_to_shape() - Resample to target shape
• reorient() - Reorient to standard orientation (RAS, LPS, etc.)
• crop_or_pad() - Crop or pad to target shape
• flip() - Flip along specified axes

Random Augmentation

• random_flip() - Random axis flipping
• random_gaussian_noise() - Additive Gaussian noise
• random_intensity_scale() - Random intensity scaling
• random_intensity_shift() - Random intensity offset
• random_rotate_90() - Random 90-degree rotations
• random_gamma() - Random gamma correction
• random_augment() - Combined augmentation pipeline

Performance

medrs uses several optimization strategies:

• SIMD: Trilinear interpolation uses AVX2/SSE for 8-way parallel processing
• Parallel Processing: Rayon-based parallelism for large volumes
• Lazy Evaluation: Transform pipelines compose operations before execution
• Memory Mapping: Large files are memory-mapped to avoid full loads
• Buffer Pooling: Reusable buffers reduce allocation overhead

Examples

See the examples/ directory for:

• basic/ - Loading, transforms, and saving
• integrations/ - PyTorch, MONAI, JAX integration
• advanced/ - Async pipelines, custom transforms

Testing

# Rust tests
cargo test

# Python tests
pytest tests/

# Benchmarks (requires torch, monai, torchio)
python benchmarks/bench_medrs.py --quick
python benchmarks/bench_monai.py --quick
python benchmarks/bench_torchio.py --quick

# Generate benchmark plots
python benchmarks/plot_results.py

License

medrs is dual-licensed under MIT and Apache-2.0. See LICENSE for details.

Contributing

See CONTRIBUTING.md for guidelines.

Maintainer

Liam Chalcroft (liam.chalcroft.20@ucl.ac.uk)