{"categories":[{"label":"Scientific/Engineering","url":"https://skillfed.io/packages/category/scientific-engineering/8"},{"label":"Artificial Intelligence","url":"https://skillfed.io/packages/category/scientific-engineering-artificial-intelligence/10"},{"label":"Medical Science Apps.","url":"https://skillfed.io/packages/category/scientific-engineering-medical-science-apps"},{"label":"Image Processing","url":"https://skillfed.io/packages/category/scientific-engineering-image-processing"}],"enrichment":{"capability":"TorchIO reads, preprocesses, augments, and samples 3D medical images for deep learning with PyTorch, offering both standard computer vision transforms and domain-specific medical imaging operations like MRI artifact simulation.","skillfed_tags":["medical-imaging","3d-volumetric","data-augmentation"],"use_cases":["Train deep learning models on 3D MRI or CT scans with realistic medical image augmentation.","Preprocess volumetric medical images for patch-based segmentation or classification tasks.","Simulate MRI artifacts during training to improve model robustness to real acquisition imperfections.","Load and normalize medical images in standard formats for PyTorch pipelines.","Sample spatial patches from large 3D volumes for memory-efficient training on GPUs."],"what_it_does":"TorchIO is a specialized library for working with 3D medical images in PyTorch-based deep learning pipelines. It handles the full workflow from loading medical image formats (via nibabel) through preprocessing, augmentation, and sampling patches for training. The library includes both standard transforms\u2014affine transformations, flips, noise injection\u2014and domain-specific operations that simulate real MRI artifacts like magnetic field inhomogeneity effects and k-space motion artifacts, making it valuable for training models on realistic medical imaging data.\n\nThe package is designed for efficiency in patch-based training, where 3D volumes are sampled into smaller regions for memory-constrained training. It integrates with PyTorch's ecosystem and provides a queue-based sampling mechanism for iterative training workflows. With 13 runtime dependencies including torch, scipy, nibabel, and rich, it brings together the tools needed for medical image I/O, numerical operations, and user-friendly progress reporting in a single coherent interface.","worth_installing":"Yes. TorchIO is actively maintained, permissively licensed, and has low install friction. It fills a genuine gap for medical imaging practitioners using PyTorch by providing domain-specific transforms and efficient 3D I/O that would otherwise require custom code. The library is well-established with strong community adoption (2431 stars) and no known security vulnerabilities. Install it if you're building medical imaging models with PyTorch."},"id":"torchio","links":{"html":"https://skillfed.io/packages/torchio","md":"https://skillfed.io/packages/torchio.md","pypi":"https://pypi.org/project/torchio/"},"maintenance":{"status":"active"},"meta":{"latest_release":"2026-06-02","license_spdx":"Apache-2.0","license_treatment":"permissive","name":"torchio","python_support":"supports_current","summary":"Tools for medical image processing with PyTorch"},"popularity":{"monthly_downloads":91096,"position":13541,"tier":"top_15000"},"security":{"n_vulnerabilities":0},"version":"1.2.1"}