Processing Module

The processing module provides tools for image preprocessing and analysis, including denoising, segmentation, and partitioning capabilities for various microscopy modalities.

This module contains three main components:

• Denoising: Advanced noise reduction algorithms for microscopy data

• Segmentation: Deep learning-based and classical cellular structure segmentation methods for multiple imaging modalities

• Partitioning: Tools for spatial organization partitioning

The processing module is designed to handle data from various microscopy techniques including:

• Soft X-ray Tomography (SXT)

• Cryo-Electron Tomography (cryo-ET)

• Structured Illumination Microscopy (SIM)

• Cryo-Electron Microscopy (cryo-EM)

• Confocal microscopy

• Airyscan microscopy

• Denoising Module
  • Overview
  • N2V Class (Noise2Void)
  • N2N Class (Noise2Noise)
  • Visualization and Quality Assessment
  • Performance Tips
• Segmentation Module
  • Overview
  • Architecture
  • Key Components
  • Model Architecture and Methods
  • Model Selection Guide
  • Quick Start
  • Submodules
• Partitioning Module
  • Overview
  • Partitioning Class
  • Visualization Functions
  • Complete Workflow Example
  • Output Data Formats