MedicalZooPytorch
A PyTorch framework for multi-modal 2D and 3D medical image segmentation.
PyTorch deep learning framework for 3D multi-modal medical image segmentation
MedicalZooPytorch is an open-source deep learning library designed for multi-modal 2D and 3D medical image segmentation. It suits researchers and developers working with brain MRI scans, challenge datasets, and volumetric medical data in PyTorch.
What it is
It implements standard 3D architectures like U-Net3D, V-Net, ResNet3D-VAE, and DenseVoxelNet alongside data loaders for benchmark datasets such as BraTS, iSeg, and MRBrains. The project also provides preprocessing pipelines, confusion matrix support, and optional COVID-19 classification and segmentation models.
Who it's for
It suits researchers and graduate students familiar with PyTorch who need reference architectures for volumetric medical imaging. It does not suit clinicians seeking a ready-to-use clinical viewing or diagnostic software without programming knowledge.
Key features
Multiple 3D Architectures
Includes implementations of U-Net3D, V-Net, DenseVoxelNet, HighResNet3D, and HyperDense-Net.
Built-in Data Loaders
Provides data loaders for BraTS 2018/2019, iSeg, MRBrains, IXI, and Gleason challenge datasets.
COVID-19 Modules
Includes models and dataset references for COVID-19 chest radiography classification and lung segmentation.
Google Colab Support
Offers quickstart notebooks allowing you to run models on cloud GPUs free of charge.
Up and running in 3 steps
Our honest verdict
What's great
- Implements ten distinct 3D deep learning architectures out of the box
- Bundles data loaders for major challenge datasets like BraTS and iSeg
- Provides ready-to-run training scripts for specific medical imaging benchmarks
- Includes Google Colab notebooks for hardware-free experimentation
Where it falls short
- Requires familiarity with Python, PyTorch, and deep learning workflows
- Training 3D convolutional networks demands substantial GPU compute resources
- Project has faced development pauses due to hardware constraints
- Documentation requires referencing external academic papers and thesis texts
Pricing & license
The software is free under its MIT license. There are no paid hosted options provided by the maintainers, so your only costs involve local hardware or cloud GPU compute resources.
Open remote AI-training roles
874 open remote AI-training roles, updated November 15, 2026; most pay $20–$170 an hour. Zekai lists them free; the application goes to the employer’s platform.
Alternatives
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FAQ
What datasets are natively supported?
The framework includes data loaders for iSeg 2017/2019, MICCAI BraTS 2018/2019, MRBrains 2018, IXI, and the MICCAI 2019 Gleason challenge.
Can I run this without a local GPU?
Yes, you can use the provided Google Colab notebook to run the project using a cloud GPU device.
What deep learning backend does it use?
MedicalZooPytorch is built entirely on PyTorch and requires Python.
