wolny/pytorch-3dunet

Health v2 · maintenance only

Adoption not part of the score

2416 stars · 563 forks observed · 2026-08-28

What it is AI-extracted, prompt v1, taxonomy v1, 2026-08-30, confidence not recorded

A PyTorch implementation of 3D U-Net and its variants (residual, squeeze-and-excitation) for volumetric semantic segmentation, with 2D U-Net support as well. It supports training for binary/multi-class segmentation and regression tasks like denoising, using HDF5 input data.

Use cases

• segment 3D volumetric microscopy data
• train a 3D U-Net in PyTorch
• semantic segmentation of medical volumes
• denoise volumetric data with a U-Net
• run connectomics boundary segmentation
• train a 2D U-Net for cell segmentation

When to choose

• you need volumetric (3D) semantic segmentation with a proven U-Net implementation
• your data is stored in HDF5 and you want configurable training with dice loss and GroupNorm variants
• you work in microscopy, connectomics, or medical imaging segmentation

When to avoid

• you need instance segmentation or out-of-the-box pretrained models for general images
• you want a lightweight inference-only tool without a training pipeline
• your project is not Python/PyTorch based

Facets

library · maturity active

machine-learning deep-learning image-processing computer-vision deep-learning computer-vision image-processing bioinformatics healthcare python windows unet semantic-segmentation volumetric-data 3d-segmentation pytorch hdf5 connectomics microscopy medical-imaging dice-coefficient linux macos gpu

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