emcfsys 0.2.2

Towards foundation models for EM images analysis. EMCellFiner and EMCellFound are two foundation models trained based on a 4 million EM images dataset.

EMCFsys

Towards foundation models for EM images analysis. emcfsys provides a comprehensive toolkit and a napari plugin for Electron Microscopy (EM) image restoration and segmentation, featuring EMCellFiner and EMCellFound—two foundation models pre-trained on a massive dataset of 4 million EM images.

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✨ Key Features

1. Key Features for emcfsys

• Pre-trained Backbones: Optimized on 4M+ EM images for superior feature extraction: EMCellFound and EMCellFiner.
• Code-Free Train and Inference: Train the segmentation model using Pre-trained Backbones and inference in the GUI without Any Code.
• Code-Free and Training-Free Image restoration/super-resolution pipline: Training-Free pipline for EM image restoration/super-resolution.

2. Segmentation pipline using Foundation model

• Pre-trained Backbones:
  • EMCellFound Core: Our foundational backbones are built upon state-of-the-art ViT (Vision Transformer) and ConvNext architectures. These models are pre-trained using advanced self-supervised learning frameworks, specifically MAE (Masked Autoencoders) and DINOv3, ensuring robust feature representation for complex electron microscopy data.
  • Continuous Evolution: We are committed to the iterative refinement of our models. We periodically retrain EMCellFound using superior architectures, optimized algorithms, and larger-scale datasets to ensure the system consistently delivers peak performance.
  • Timm Library Integration: To provide maximum flexibility, the system fully supports a wide range of popular pre-trained models from the timm library, allowing users to select the most suitable backbone for their specific research needs.
• Segmentation Heads: Includes U-Net, PSPNet, DeepLabv3+, and UperNet.
• Finetune Models: We support to finetune the EMCellFound/Timm-model to make specialize segmentation pipline.
• Inference 2D/3D images: We support to load the Checkpoint and inference image in 2D and 3D.
• Tailored Training Strategies: Detailed specifications of our training configurations can be found in Functions notebook. Key components include:
  • Data Augmentation: Robust Dataset class with multiple transform strategies.
  • Loss Functions: Integrated CrossEntropy and Dice Loss (Focal Loss coming soon).
  • Metrics: Real-time evaluation using IoU, Accuracy, and F1-Score.
  • Smart Checkpoints: Automatically preserves the best-performing model (Best IoU) and prunes redundant files.

3. Image restoration/super-resolution pipline using Foundation model

• Retraining-free: We train the image restoration/super-resolution model EMCellFiner on 4M+ EM images, thus EMCellFiner has robust performance，can restore/super-resolution for most of EM images and make them finer.
• Single-image: We support restore/super-resolution in the GUI using GPU/CPU, and show in the GUI.
• Multi-image: We also support to restore/super-resolution the images in the folder, and output to another folder.

4. Tools

• Annotation Support: Built-in utility to convert Labelme JSON annotations to Semantic Segmentation masks.

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📖Installation

To leverage the full potential of the EMCellFiner and EMCellFound foundation models, an NVIDIA GPU(We suggest > RTX3090) is highly recommended.

1. Environment Preparation

We recommend using Conda/miniconda to create an isolated environment. We suggest using python>=3.11 (We have successfully test the pipline in python version 3.8\3.9\3.10\3.11)

# Create a new environment named 'emcfsys' with Python> 3.11
conda create -n emcfsys python=3.11 -y
# Activate the environment
conda activate emcfsys

2. Install PyTorch with GPU Support

emcfsys requires PyTorch > 1.3. For optimal performance, we recommend PyTorch 2.0+. Choose the command matching your CUDA version:

# For Linux and Window:
pip install torch==2.4.1 torchvision==0.19.1 torchaudio==2.4.1 --index-url https://download.pytorch.org/whl/cu118

Or follow link to select the torch version https://pytorch.org/get-started/previous-versions/

3. Install napari and emcfsys

3.1 You can follow the napari GUI installation document:https://github.com/napari/napari Or use the follow pipline

pip install "napari[pyqt6, optional]" 

Then you can install emcfsys via [pip]:

pip install emcfsys 

You can also install emcfsys in the napari-plugin-store

At last, install necessary components:

pip install labelme timm opencv-python einops shapely albumentations

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📖 Quick Start

1. Use as a napari Plugin
2. Launch napari: napari
3. Navigate to: Plugins -> emcfsys
4. Load your image and select EMCellFiner for instant enhancement.

📖 Tutorial

All tutorials and feature descriptions can be found in the tutorial documentation (click me!).

License

Distributed under the terms of the [GNU GPL v3.0] license, "emcfsys" is free and open source software

Issues

If you encounter any problems, please [file an issue] along with a detailed description.

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Towards foundation models for EM images analysis. EMCellFiner and EMCellFound are two foundation models trained based on a 4 million EM images dataset.

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