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OCTOPI 🐙🐙🐙

Object deteCTion Of ProteIns. A deep learning framework for Cryo-ET 3D particle picking with autonomous model exploration capabilities.

Octopi

🧬 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

🙋 Getting Help