SABER⚔️

Segment Anything Based Expert Recognition is a robust platform designed for autonomous segmentation of organelles from cryo-electron tomography (cryo-ET) or electron microscopy (EM) datasets.

Introduction

Leveraging foundational models, SABER enables segmentation directly from video-based training translated into effective 3D tomogram analysis. Users can utilize zero-shot inference with morphological heuristics or enhance prediction accuracy through data-driven training.

💫 Key Features

• 🔍 Zero-shot segmentation: Segment EM/cryo-ET data without explicit retraining, using foundational vision models.
• 🖼️ Interactive GUI for labeling: Intuitive graphical interface for manual annotation and segmentation refinement.
• 🧠 Expert-driven classifier training: Fine-tune segmentation results by training custom classifiers on curated annotations.
• 🧊 3D organelle segmentation: Generate volumetric segmentation masks across tomographic slices.

🚀 Getting Started

Installation

Saber is available on PyPI and can be installed using pip:

pip install saber-em

⚠️ Note:

• By default, the GUI is not included in the base installation. To enable the graphical interface for manual annotation, install with:

pip install saber-em[gui]

• One of the current dependencies is currently not working with pip 25.1. We recommend using pip 25.2 or higher when installing saber:

pip install --upgrade "pip>=25.2"

Basic Usage

SABER provides a clean, scriptable command-line interface. Run the following command to view all available subcommands:

saber --help

📚 Documentation

For detailed documentation, tutorials, CLI and API reference, visit our documentation

🤝 Contributing

This project adheres to the Contributor Covenant code of conduct. By participating, you are expected to uphold this code. Please report unacceptable behavior to opensource@chanzuckerberg.com.

🔒 Security

If you believe you have found a security issue, please responsibly disclose by contacting us at security@chanzuckerberg.com.