MIC-DKFZ/batchgenerators

Health v2 · maintenance only

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Adoption not part of the score

1173 stars · 230 forks observed · 2026-08-28

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

A Python framework for data augmentation of 2D and 3D images, developed by the German Cancer Research Center for medical image classification and segmentation. It provides spatial, color, and noise transforms composable via a pipeline, plus a multithreaded augmenter for data loading.

Use cases

• augment 3D medical images for segmentation training
• apply elastic deformations and rotations to image batches
• simulate misalignment between multi-modal image channels
• build a multithreaded data loading pipeline for deep learning
• add Gaussian and Rician noise to training images
• augment 2D images for classification models

When to choose

• you train models on 2D or 3D images, especially medical imaging
• you need augmentations that work on volumetric (3D) data
• you want composable transforms with a multithreaded batch loader

When to avoid

• you only need standard 2D photo augmentations for natural images
• you want a framework with GPU-side augmentation
• you need a general-purpose data loading library outside image augmentation

Facets

library · maturity stable

machine-learning image-processing data-generation machine-learning deep-learning computer-vision image-processing healthcare python cross-platform data-augmentation medical-imaging 3d-images segmentation multithreaded-dataloader

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