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<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8" />
<meta name="viewport" content="width=device-width, initial-scale=1.0" />
<title>TorchIO: Medical imaging for AI in PyTorch</title>
<meta
name="description"
content="TorchIO is an open-source Python library for efficient loading, preprocessing, augmentation, and patch-based sampling of 3D medical images in AI, built on top of PyTorch."
/>
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<link rel="canonical" href="https://torchio.org/" />
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property="og:description"
content="Efficient loading, preprocessing, augmentation, and patch-based sampling of 3D medical images, built on top of PyTorch."
/>
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content="TorchIO: Medical imaging for AI in PyTorch"
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content="Efficient loading, preprocessing, augmentation, and patch-based sampling of 3D medical images, built on top of PyTorch."
/>
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name="twitter:image"
content="https://torchio.org/assets/img/torchio-logo.png"
/>
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src="assets/img/torchio-logo-64.png"
alt="TorchIO logo"
width="28"
height="28"
decoding="async"
/>
TorchIO
</a>
<nav class="nav-links">
<a href="#features" class="hide-sm">Features</a>
<a href="#augmentations" class="hide-sm">Augmentations</a>
<a href="https://docs.torchio.org/dev/">Docs</a>
<a href="https://github.com/TorchIO-project/torchio" class="hide-sm"
>GitHub</a
>
<a
class="btn btn-primary nav-cta"
href="https://docs.torchio.org/dev/get-started/installation/"
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>
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<main id="content">
<!-- Hero -->
<section class="hero">
<div class="container hero-grid">
<div class="hero-copy">
<h1>
Medical imaging for <span class="accent">AI</span>.
</h1>
<p class="hero-lede">
An open-source Python library for efficient loading, preprocessing,
augmentation, and patch-based sampling of 3D medical images,
built on top of PyTorch.
</p>
<div class="hero-actions">
<a
class="btn btn-primary"
href="https://docs.torchio.org/dev/get-started/quickstart/"
>
Get started
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</div>
<div class="install" aria-label="Install command">
<code id="install-cmd"
><span class="prompt">$</span> pip install torchio</code
>
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aria-label="Copy install command"
data-copy="pip install torchio"
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</div>
<!-- Hero code card -->
<div class="code-card reveal">
<div class="code-bar">
<span class="code-file">quickstart.py</span>
</div>
<pre><code><span class="tok-k">import</span> torchio <span class="tok-k">as</span> tio
dirs = [<span class="tok-s">"sub-01"</span>, <span class="tok-s">"sub-02"</span>, <span class="tok-s">"sub-03"</span>, <span class="tok-s">"sub-04"</span>]
<span class="tok-c"># Lazy: only headers are read until .data is accessed</span>
subjects = [
tio.<span class="tok-f">Subject</span>(
t1=tio.<span class="tok-f">ScalarImage</span>(<span class="tok-s">f"{d}/t1.nii.gz"</span>),
seg=tio.<span class="tok-f">LabelMap</span>(<span class="tok-s">f"{d}/seg.nii.gz"</span>),
)
<span class="tok-k">for</span> d <span class="tok-k">in</span> dirs
]
<span class="tok-c"># Composable augmentation pipeline</span>
transform = tio.<span class="tok-f">Compose</span>([
tio.<span class="tok-f">Flip</span>(flip_probability=0.5),
tio.<span class="tok-f">Affine</span>(degrees=(-15, 15)),
tio.<span class="tok-f">Standardize</span>(),
tio.<span class="tok-f">Noise</span>(std=(0, 0.1)),
])
<span class="tok-c"># Augment a whole batch on the GPU in one call</span>
batch = tio.SubjectsBatch.<span class="tok-f">from_subjects</span>(subjects).to(<span class="tok-s">"cuda"</span>)
augmented = <span class="tok-f">transform</span>(batch)</code></pre>
</div>
</div>
</section>
<!-- Ecosystem strip -->
<div class="strip">
<div class="container">
<span class="label">Plays well with</span>
<div class="names">
<a href="https://pytorch.org">PyTorch</a>
<a href="https://project-monai.github.io/">MONAI</a>
<a href="https://lightning.ai">PyTorch Lightning</a>
<a href="https://simpleitk.org">SimpleITK</a>
<a href="https://nipy.org/nibabel/">NiBabel</a>
<a href="https://zarr.dev">Zarr</a>
</div>
</div>
</div>
<!-- Features -->
<section class="section" id="features">
<div class="container">
<div class="section-head reveal">
<span class="eyebrow">Why TorchIO</span>
<h2>Everything you need for medical image pipelines</h2>
<p>
Purpose-built for volumetric data, from a single scan to datasets
far larger than memory.
</p>
</div>
<div class="features-grid">
<a
class="card card-link reveal"
href="https://docs.torchio.org/dev/concepts/transforms/"
>
<span class="icon">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path d="M4 4h16v16H4z" />
<path d="M4 9h16M9 4v16" />
</svg>
</span>
<h3>Preprocessing & augmentation</h3>
<p>
Spatial and intensity transforms designed for 3D medical images,
fully composable into reproducible pipelines.
</p>
<span class="card-more">Learn more</span>
</a>
<a
class="card card-link reveal"
href="https://docs.torchio.org/dev/reference/patches/"
>
<span class="icon">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
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<rect x="3" y="14" width="7" height="7" rx="1" />
<rect x="14" y="14" width="7" height="7" rx="1" />
</svg>
</span>
<h3>Patch-based pipelines</h3>
<p>
Queues, samplers, and aggregators for training and inference on
volumes that are too large to fit in memory.
</p>
<span class="card-more">Learn more</span>
</a>
<a
class="card card-link reveal"
href="https://docs.torchio.org/dev/concepts/data-model/"
>
<span class="icon">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path d="M12 2v20M2 12h20" />
<circle cx="12" cy="12" r="9" />
</svg>
</span>
<h3>Lazy loading at scale</h3>
<p>
Read only the headers, or just the patch you need, from local
files, remote URLs, cloud storage, or Zarr stores.
</p>
<span class="card-more">Learn more</span>
</a>
<a
class="card card-link reveal"
href="https://docs.torchio.org/dev/how-to/dataloader/"
>
<span class="icon">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path d="M22 12a10 10 0 1 1-20 0 10 10 0 0 1 20 0Z" />
<path d="M12 6v6l4 2" />
</svg>
</span>
<h3>Built on PyTorch</h3>
<p>
Drop-in with the PyTorch <code>DataLoader</code>, and a natural fit
alongside MONAI and PyTorch Lightning.
</p>
<span class="card-more">Learn more</span>
</a>
<a
class="card card-link reveal"
href="https://docs.torchio.org/dev/reference/transforms/"
>
<span class="icon">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path d="m9 12 2 2 4-4" />
<path d="M12 3 4 6v6c0 5 3.5 7.5 8 9 4.5-1.5 8-4 8-9V6l-8-3Z" />
</svg>
</span>
<h3>Reproducible & GPU-ready</h3>
<p>
Deterministic, serializable transforms that run on CPU, CUDA, or
Apple MPS, and batch across subjects.
</p>
<span class="card-more">Learn more</span>
</a>
<a
class="card card-link reveal"
href="https://doi.org/10.1016/j.cmpb.2021.106236"
>
<span class="icon">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path d="M12 21s-7-4.5-7-10a4 4 0 0 1 7-2.6A4 4 0 0 1 19 11c0 5.5-7 10-7 10Z" />
</svg>
</span>
<h3>Open source & cited</h3>
<p>
Apache-2.0 licensed, peer-reviewed, and developed in the open by a
friendly community of contributors.
</p>
<span class="card-more">Read the paper</span>
</a>
</div>
</div>
</section>
<!-- Augmentations showcase -->
<section class="section" id="augmentations">
<div class="container">
<div class="section-head reveal">
<span class="eyebrow">Augmentations</span>
<h2>Realistic MRI artifacts, one transform away</h2>
<p>
TorchIO ships physics-based augmentations that reproduce real MRI
artifacts. Pick one, then move the slider to compare the original
scan with the augmented result.
</p>
</div>
<div class="aug reveal">
<div class="aug-slider" id="aug-slider" style="--pos: 50%">
<img
class="aug-img aug-after"
id="aug-after"
src="assets/aug/motion_after.webp"
alt="Brain scan with the augmentation applied"
width="1200"
height="602"
decoding="async"
/>
<img
class="aug-img aug-before"
id="aug-before"
src="assets/aug/motion_before.webp"
alt="Original brain scan"
width="1200"
height="602"
decoding="async"
/>
<span class="aug-tag aug-tag--before">Original</span>
<span class="aug-tag aug-tag--after">Augmented</span>
<div class="aug-divider" aria-hidden="true">
<span class="aug-handle">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path d="M9 7l-5 5 5 5M15 7l5 5-5 5" />
</svg>
</span>
</div>
<input
class="aug-range"
id="aug-range"
type="range"
min="0"
max="100"
value="50"
aria-label="Reveal the original versus the augmented scan"
/>
</div>
<div class="aug-panel">
<div class="aug-tabs" aria-label="MRI augmentations">
<button
class="aug-tab is-active"
type="button"
aria-pressed="true"
data-aug="motion"
>
Motion
</button>
<button
class="aug-tab"
type="button"
aria-pressed="false"
data-aug="ghosting"
>
Ghosting
</button>
<button
class="aug-tab"
type="button"
aria-pressed="false"
data-aug="spike"
>
Spike
</button>
<button
class="aug-tab"
type="button"
aria-pressed="false"
data-aug="bias"
>
Bias field
</button>
<button
class="aug-tab"
type="button"
aria-pressed="false"
data-aug="noise"
>
Noise
</button>
<button
class="aug-tab"
type="button"
aria-pressed="false"
data-aug="elastic"
>
Elastic
</button>
</div>
<code class="aug-code" id="aug-code">tio.Motion()</code>
<p class="aug-desc" id="aug-desc" aria-live="polite">
Simulates patient motion during acquisition, adding blurring and
ghosting from inconsistent k-space lines.
</p>
<a class="aug-link" href="https://docs.torchio.org/dev/concepts/transforms/">
Learn how transforms work
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path d="M5 12h14M13 6l6 6-6 6" />
</svg>
</a>
</div>
</div>
</div>
</section>
<!-- Example callout -->
<section class="section" id="example" style="padding-top: 0">
<div class="container">
<div class="cta reveal">
<span class="eyebrow">Pipeline</span>
<h2>From scan to batch in a few lines</h2>
<p>
Compose your transforms once, then augment whole batches of subjects
on the GPU in a single call.
</p>
<div class="pipeline">
<div class="pipe-step">
<span class="pipe-num">1</span>
<span class="pipe-icon">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path d="m12 2 9 5-9 5-9-5 9-5Z" />
<path d="m3 12 9 5 9-5" />
<path d="m3 17 9 5 9-5" />
</svg>
</span>
<span class="pipe-title">Load subjects</span>
<span class="pipe-sub">Lazily, from files or the cloud</span>
</div>
<span class="pipe-arrow" aria-hidden="true">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path d="M5 12h14M13 6l6 6-6 6" />
</svg>
</span>
<div class="pipe-step pipe-step--wide">
<span class="pipe-num">2</span>
<span class="pipe-icon">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path
d="M13 3l2.2 5.8L21 11l-5.8 2.2L13 19l-2.2-5.8L5 11l5.8-2.2L13 3Z"
/>
<path d="M5 3v3M3.5 4.5h3M6 18v3M4.5 19.5h3" />
</svg>
</span>
<span class="pipe-title">Compose transforms</span>
<span class="pipe-chips">
<span>Flip</span>
<span>Affine</span>
<span>Standardize</span>
<span>Noise</span>
</span>
</div>
<span class="pipe-arrow" aria-hidden="true">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<path d="M5 12h14M13 6l6 6-6 6" />
</svg>
</span>
<div class="pipe-step">
<span class="pipe-num">3</span>
<span class="pipe-icon">
<svg
viewBox="0 0 24 24"
fill="none"
stroke="currentColor"
stroke-width="2"
stroke-linecap="round"
stroke-linejoin="round"
aria-hidden="true"
focusable="false"
>
<rect x="4" y="4" width="16" height="16" rx="2" />
<rect x="9" y="9" width="6" height="6" />
<path
d="M9 1.5v2.5M15 1.5v2.5M9 20v2.5M15 20v2.5M20 9h2.5M20 15h2.5M1.5 9H4M1.5 15H4"
/>
</svg>
</span>
<span class="pipe-title">Augment the batch</span>
<span class="pipe-sub">One call, on CPU or GPU</span>
</div>
</div>
<div class="cta-actions">
<a
class="btn btn-primary"
href="https://docs.torchio.org/dev/get-started/quickstart/"
>Read the quickstart</a
>
<a
class="btn btn-ghost"
href="https://docs.torchio.org/dev/tutorials/first-pipeline/"
>Browse tutorials</a
>
</div>
</div>
</div>
</section>
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<p>
Tools for medical image processing with PyTorch. Free and
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</p>
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<h3>Docs</h3>
<a href="https://docs.torchio.org/dev/get-started/installation/"
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