MrGiovanni/UNetPlusPlus

Health v2 · maintenance only

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Adoption not part of the score

2679 stars · 565 forks observed · 2026-08-28

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

Official implementation of UNet++, a nested U-Net architecture for medical image segmentation, in both Keras and PyTorch. It redesigns skip connections with dense decoder connections to exploit multiscale features and address the unknown optimal depth of the original U-Net.

Use cases

• segment medical images like CT and MRI scans
• implement UNet++ for biomedical image segmentation
• train a nested U-Net with redesigned skip connections
• segment organs or tumors in medical imaging datasets
• reproduce results from the UNet++ TMI paper
• compare U-Net variants for semantic segmentation research

When to choose

• you need the canonical, paper-backed UNet++ implementation for medical image segmentation
• you work with CT or MRI data and want dense skip connections across decoder depths
• you want both Keras and PyTorch reference code from the original authors
• you are researching segmentation architectures and need a well-cited baseline

When to avoid

• you need a general-purpose segmentation toolkit beyond the UNet++ architecture
• you require a permissively licensed library for commercial embedding, since the license is not a standard OSI license
• you want a maintained production pipeline with pretrained weights and inference APIs rather than research code
• your project is not image segmentation related

Facets

library · maturity stable

machine-learning deep-learning image-processing computer-vision deep-learning computer-vision healthcare python cross-platform unet-plus-plus semantic-segmentation medical-image-segmentation nested-unet skip-connections biomedical-imaging keras pytorch research-code ieee-tmi medical-imaging

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