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OMERO Annotate.AI#

OMERO annotate.AI is a Python package that provides tools for reproducible AI workflows (annotation, training and inference) using OMERO (Open Microscopy Environment) data repositories. The package directly connects OMERO datasets with AI dataset annotation tools such as micro-sam annotator tool in napari.

Key Features#

  • OMERO connection and annotation workflow widgets within Jupyter notebooks
  • Pydantic model validated YAML configuration files to track the annotation and training workflow
  • Direct integration of micro-SAM annotation of OMERO data
  • Saving annotations and annotation configuration back into OMERO (OMERO.table, YAML)
  • Preparation of training data for Biapy and DL4MicEverywhere
  • 3D volumetric annotation support for z-stack processing
  • Reproducible AI workflows with detailed tracking and validation

Workflows Supported#

Interactive Widget Workflows#

  • OMERO Connection Widget - Secure connection to OMERO servers with credential management
  • Annotation Pipeline Widget - Visual configuration of annotation workflows
  • Progress Tracking - Real-time monitoring of annotation progress

OMERO Connection Widget OMERO connection widget for secure server authentication

Annotation Pipeline Widget
Annotation pipeline widget for visual workflow configuration

Annotation Workflows#

  • Interactive widget-based annotation using OMERO connection and workflow widgets
  • YAML configuration-driven annotation for reproducible batch processing
  • micro-SAM assisted annotation with automatic segmentation
  • Cellpose integration for cell segmentation workflows
  • 3D volumetric annotation for z-stack data

Training Workflows#

  • BiaPy integration for deep learning model training
  • Training data preparation with automatic train/validation splits
  • micro-SAM model fine-tuning on custom datasets

Quality Control#

  • Annotation validation and quality metrics
  • Progress tracking with detailed status reporting
  • Resume functionality from previous annotation sessions

Quick Start#

For a quick start, see our Installation Guide for detailed setup instructions.

Basic 2-Widget Workflow#

from omero_annotate_ai import create_omero_connection_widget, create_workflow_widget, create_pipeline

# Step 1: Connect to OMERO
conn_widget = create_omero_connection_widget()
conn_widget.display()
conn = conn_widget.get_connection()

# Step 2: Configure workflow
workflow_widget = create_workflow_widget(connection=conn)
workflow_widget.display()
config = workflow_widget.get_config()

# Step 3: Run pipeline
pipeline = create_pipeline(config, conn)
table_id, processed_images = pipeline.run_full_workflow()

Configuration-Based Workflow#

from omero_annotate_ai.core.annotation_config import load_config
from omero_annotate_ai.core.annotation_pipeline import create_pipeline
from omero_annotate_ai.omero.simple_connection import create_connection

# Load configuration from YAML
config = load_config("annotation_config.yaml")

# Connect to OMERO
conn = create_connection(host="omero.server.com", user="username")

# Run annotation pipeline  
pipeline = create_pipeline(config, conn)
results = pipeline.run_full_workflow()

User Guides#

Getting Started#

Step-by-Step Tutorials#

🚀 micro-SAM Annotation Pipeline

Complete

Complete workflow tutorial covering OMERO connection, configuration, and micro-SAM annotation execution.

Start Tutorial

🔬 Cellpose Integration

Planned

Learn to use Cellpose models for cell segmentation workflows within the OMERO framework.

View Tutorial

📊 Training Data Preparation

Planned

Prepare high-quality training datasets from OMERO annotations for machine learning model development.

View Tutorial

🧠 BiaPy Integration

Planned

Train custom deep learning models using BiaPy with data prepared from OMERO annotations.

View Tutorial

Advanced Workflows#

Configuration Reference#

Community & Support#