Cellable

Cell Organelle Labeling with Python

Overview

This application is an extended version of Labelme, designed for interactive 2D/3D segmentation and annotation of electron microscopy (EM) and other scientific images. It supports:

• Viewing and annotating 2D slices and 3D volumes
• Loading TIFF stacks for volumetric data
• Automatic AI-assisted segmentation
• Manual mask editing and refinement
• 3D rendering via VTK

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Installation

1. Requirements

• Python 3.8+
• GPU recommended for AI-assisted segmentation
• OS: Linux, macOS, or Windows

2. Install Dependencies

Key dependencies include:

• PyQt5 – GUI framework
• vtk – 3D rendering
• tifffile – TIFF image I/O
• cc3d – connected component analysis
• scikit-image, scipy, numpy – image processing
• imgviz – visualization utilities

```bash git clone https://github.com/luckieucas/cellable.git cd cellable

Setup conda

conda create --name cellable python=3.9 conda activate cellable

Install dependencies

pip install -r requirements.txt

Install cellable

pip install -e . ```

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📚 User Tutorial - Cellable 3D Segmentation Edition

🚀 Getting Started

Launch the Application

bash conda activate cellable cellable

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🖥️ Interface Overview

Main Window Layout

• Toolbar: File operations, AI segmentation, view adjustments
• Canvas Area: Displays current image or 3D slice
• Label List: Shows all current annotations
• Status Bar: Displays slice index, zoom level, current tool

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📁 Data Loading & Supported Formats

Supported File Formats

• Images: .png, .jpg, .tif, .tiff
• Volume Data: Multi-page TIFF stacks

Loading Data Steps

1. Open Image/Stack: File → Open
2. For 3D TIFF stacks, a slider will appear for slice navigation

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️ View Navigation & Operations

Basic Operations

• Mouse Scroll: Change zoom level
• Arrow Keys/Slider: Move between slices
• Drag: Pan the view

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✏️ Annotation Tools

1. Polygon Tool - Manual Contour Drawing

• Click on canvas to create vertices
• Double-click to complete drawing
• Right-click to edit vertices

2. Mask Tool - Region Painting

• Select brush size
• Paint mask regions
• Use eraser to remove areas

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🤖 AI-Assisted Segmentation

SAM (Segment Anything Model) Segmentation

1. Select the AI tool
2. Click inside the region of interest
3. Automatic segmentation generation

Efficient SAM - Fast Segmentation

• Faster segmentation speed
• Suitable for batch processing

Text-to-Annotation Conversion

• Input descriptive text
• Automatic annotation generation

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🔧 Mask Editing & Optimization

Shape Editing

• Move, resize, or delete shapes
• Merge or split regions
• Adjust brightness/contrast

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🌊 Watershed Segmentation - Instance Separation

Find False Merge Feature

1. Enter the target label ID in the Label ID input field on the right
2. Navigate to a slice containing adhered instances
3. Click the waterz button
4. Automatic boundary computation and view refresh

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🎨 3D Rendering & Visualization

VTK 3D Viewer

• View → 3D Viewer
• VTK-based 3D visualization of masks
• Rotate, zoom, and inspect segmented structures

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💾 Save & Export

Saving Annotations

• File → Save stores as .json format
• Mask data can be exported as NumPy arrays

Export Formats

• JSON annotation files
• VOC dataset format
• COCO dataset format

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⌨️ Keyboard Shortcuts

| Action | Shortcut | |--------|----------| | Open File | Ctrl+O | | Save Annotation | Ctrl+S | | Zoom | Hold Cmd + Mouse Scroll | | Next Slice | D | | Previous Slice | A | | Undo | Ctrl+Z | | Redo | Ctrl+Y |

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🚀 Advanced Features

Batch Processing

• Multiple file annotation
• Automatic progress saving

Annotation Quality Control

• Overlap detection
• Completeness checking
• Statistical reports

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🎯 Features

Core Features

• ✅ 2D/3D Image Annotation
• ✅ AI-Assisted Segmentation (SAM, Efficient SAM)
• ✅ Text-to-Annotation Conversion
• ✅ Watershed Instance Separation
• ✅ 3D VTK Visualization
• ✅ Multi-format Export Support

Professional Features

• 📊 Volume Data Analysis
• ✏️ Precise Mask Editing
• 📊 Batch Processing Support

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❓ Troubleshooting & FAQ

Performance Issues

• Laggy performance: Enable GPU acceleration and close unused windows
• Memory issues: Reduce the number of simultaneously open files

Technical Issues

• Mask misalignment: Check voxel dimensions in TIFF metadata
• VTK viewer not loading: Ensure vtk and PyQt5 versions are compatible

AI Segmentation Issues

• Inaccurate segmentation: Adjust click position, use manual editing for optimization
• Model loading failure: Check network connection and model file integrity

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🔗 Advanced Tutorials

Custom Annotation Workflows

• Create annotation templates
• Set annotation rules
• Quality check procedures

Data Preprocessing

• Image enhancement
• Format conversion
• Batch renaming

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🤝 Community & Support

• GitHub Issues: Report bugs and feature requests
• Discussions: Share experiences and best practices
• Contributing Guide: Participate in project development

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Credits

This version builds upon the original Labelme and integrates:

• VTK for 3D visualization
• cc3d for connected component analysis
• AI models for auto-segmentation
• Efficient SAM for fast segmentation
• Text-to-annotation capabilities

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📖 Additional Resources

• Original Labelme Project
• SAM Model Paper
• VTK Documentation
• Electron Microscopy Image Processing Best Practices

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🔍 问题分析

GitHub README的限制： - ❌ 不支持HTML <video> 标签的视频播放 - ❌ 不支持嵌入式YouTube播放器 - ❌ 不支持JavaScript交互 - ✅ 只支持静态图片和链接

** 解决方案**

方案1: 使用YouTube缩略图 + 播放按钮图标 (推荐)

```markdown:README.md