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PlantSeg

PlantSeg is a deep learning-based tool specifically designed for cell segmentation in plant tissues, addressing the unique challenges of plant cell structure and morphology.

Overview

PlantSeg combines deep learning-based boundary prediction with graph partitioning algorithms to achieve accurate cell segmentation in complex 3D plant tissues.

Environment

Environment: plantseg

Available Tasks

Start PlantSeg GUI

Launch the PlantSeg graphical user interface:

pixi run plantseg-gui

Features

  • Deep learning segmentation: State-of-the-art neural networks trained on plant tissues
  • 3D cell segmentation: Specialized for volumetric plant imaging data
  • Pre-trained models: Multiple models trained on different plant tissues and organs
  • Graph partitioning: Advanced algorithms for separating touching cells
  • Batch processing: Process multiple datasets efficiently
  • Custom training: Ability to train models on your own plant data
  • Multi-scale analysis: Handle images at different resolutions

Supported Plant Tissues

  • Root tissues: Specialized models for root cell segmentation
  • Leaf tissues: Models optimized for leaf cellular structure
  • Shoot apical meristems: Segmentation of meristematic tissues
  • Ovules: Specialized for reproductive tissue analysis
  • General plant tissues: Multi-purpose models for various plant organs

Workflow Steps

  1. Image preprocessing: Prepare your 3D microscopy data
  2. Boundary prediction: Use neural networks to predict cell boundaries
  3. Segmentation: Apply graph partitioning to separate individual cells
  4. Post-processing: Refine segmentation results
  5. Analysis: Extract quantitative measurements from segmented cells

Getting Started

  1. Make sure you have pixi installed and this repository cloned
  2. Navigate to the repository directory
  3. Run pixi run plantseg-gui to start the PlantSeg interface
  4. Load your 3D plant imaging data and select appropriate models
  5. Run the segmentation pipeline and analyze your results!

Input Requirements

  • 3D image stacks: Confocal or light-sheet microscopy data
  • Supported formats: TIFF, H5, various microscopy formats
  • Cell wall staining: Images with clear cell boundary visualization
  • Resolution: Isotropic or near-isotropic voxel spacing recommended

Use Cases

  • Developmental biology: Track cell divisions and growth in developing organs
  • Cell morphometry: Quantify cell shape, size, and volume
  • Tissue architecture: Analyze 3D tissue organization and structure
  • Comparative studies: Compare cellular organization across different conditions
  • Growth analysis: Study cell expansion and tissue growth patterns

Resources