TorchIO-project/torchio

What it solves

TorchIO simplifies the process of preparing 3D medical images for deep learning. It addresses the challenges of efficiently reading, preprocessing, sampling, and augmenting large 3D medical datasets, which often require specialized transformations that standard computer vision libraries do not provide.

How it works

Built as a Python package for PyTorch, TorchIO provides a suite of tools to handle 3D medical images. It implements intensity and spatial transforms for data augmentation and preprocessing. This includes standard vision operations like random affine transformations and domain-specific simulations, such as MRI magnetic field inhomogeneity and k-space motion artifacts.

Who it’s for

It is designed for researchers and developers building deep learning applications for medical imaging, specifically those using PyTorch.

Highlights

• 3D Medical Image Support: Specialized tools for reading, preprocessing, and writing 3D medical images.
• Domain-Specific Augmentation: Simulates MRI-specific artifacts like ghosting, spikes, and bias fields.
• Patch-Based Training: Includes a Queue system for efficient patch-based sampling during training.
• PyTorch Integration: Seamlessly integrates into PyTorch-based deep learning pipelines.