Cellpose

Segment Anything Model for microscopy: interactive and automatic segmentation of light, electron, and fluorescence microscopy images in 2D and 3D, with domain-specific fine-tuning workflows for scientific imaging (1.5K+ stars)

Self-configuring deep learning framework for semantic segmentation of biomedical images requiring no manual hyperparameter tuning; automatically adapts preprocessing, network topology, and training parameters to achieve state-of-the-art results across 120+ international competitions and benchmarks out-of-the-box (DKFZ, Nature Methods 2021, 8.3k+ stars)

Open-source bioimage analysis platform for digital pathology and research, featuring AI-powered cell detection, tissue classification, and whole-slide image analysis with extensible scripting and plugin architecture (1.3K+ stars, actively maintained)

Deep learning-based object detection and segmentation for star-convex shapes, widely adopted for cell and nucleus segmentation in fluorescence and electron microscopy via a compact neural network architecture with non-maximum suppression and shape-based post-processing (Nature Methods 2020, 1.2K+ stars)

NVIDIA and King's College London's open-source AI toolkit for healthcare imaging, providing foundational frameworks for medical image annotation (MONAI Label), training (MONAI Core), and deployment (MONAI Deploy) across radiology, pathology, and endoscopy (8K+ stars, Apache 2.0)

Universal medical image segmentation foundation model trained on 1.57M image-mask pairs across 10 imaging modalities and 30+ cancer types (Nature Communications 2024)