Crate medrs 

medrs

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

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

Key Features

• Fast NIfTI I/O: Memory-mapped reading, crop-first loading, optimized gzip
• Transform Pipeline: Lazy evaluation with automatic fusion and SIMD acceleration
• Random Augmentation: GPU-friendly augmentations for ML training
• Python Bindings: Zero-copy numpy views, direct PyTorch/JAX tensor creation

Quick Start (Rust)

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

// Load a NIfTI image
let img = nifti::load("brain.nii.gz")?;
println!("Shape: {:?}, Spacing: {:?}", img.shape(), img.spacing());

// Resample to isotropic 1mm spacing
let resampled = resample_to_spacing(&img, [1.0, 1.0, 1.0], Interpolation::Trilinear);

// Save result
nifti::save(&resampled, "output.nii.gz")?;

Transform Pipeline

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

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

// Individual augmentations with reproducible seeds
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 pipeline
let augmented = random_augment(&img, Some(42))?;

Module Overview

• nifti: NIfTI file I/O with memory mapping and crop-first loading
• transforms: Image transforms (resampling, intensity, spatial, augmentation)
• pipeline: Transform composition with lazy evaluation
• error: Error types for the library

Re-exports

pub use error::Error;

pub use error::Result;

pub use nifti::load;

pub use nifti::save;

pub use nifti::NiftiImage;

Modules

error

Error types for medrs.

nifti

NIfTI file format support.

pipeline

Transform pipeline with lazy evaluation and automatic optimization.

transforms

Image transformation operations for medical imaging.