OCTOPI 🐙🐙🐙¶
Object deteCTion Of ProteIns. A deep learning framework for Cryo-ET 3D particle picking with autonomous model exploration capabilities.
🧬 Introduction¶
octopi addresses a critical bottleneck in cryo-electron tomography (cryo-ET) research: the efficient identification and extraction of proteins within complex cellular environments. As advances in cryo-ET enable the collection of thousands of tomograms, the need for automated, accurate particle picking has become increasingly urgent.
Our deep learning-based pipeline streamlines the training and execution of 3D autoencoder models specifically designed for cryo-ET particle picking. Built on copick, a storage-agnostic API, octopi seamlessly accesses tomograms and segmentations across local and remote environments.
🚀 Key Features¶
- Training and evaluating custom 3D U-Net models for particle segmentation
- Automatic model architecture exploration using Bayesian optimization via Optuna
- Inference for both semantic segmentation and particle localization
- Seamless integration with MLflow for experiment tracking
- Support for both CLI and Python API interfaces
- HPC cluster compatibility with SLURM integration
🔗 Quick Links¶
🙋 Getting Help¶
- Visit our GitHub repository for source code and issues