MIC-DKFZ/nnActive
nnActive -- code, benchmark and results for the largest study of Active Learning for 3D Biomedical Imaging.
This framework helps biomedical researchers and clinicians efficiently label 3D medical images for segmentation tasks. It takes raw 3D biomedical scans and partially annotated data, and outputs highly accurate segmented images with reduced manual labeling effort. This tool is designed for anyone involved in medical image analysis who needs to delineate structures in 3D scans but struggles with the time and expertise required for full manual annotation.
Use this if you need to perform 3D biomedical image segmentation and want to reduce the extensive manual labeling required for training accurate models.
Not ideal if you primarily work with 2D images or have already fully annotated large 3D datasets, as its main benefit is optimizing the annotation process.
Stars
17
Forks
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Language
Python
License
Apache-2.0
Category
Last pushed
Mar 12, 2026
Commits (30d)
0
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