EmmaSRH / UNI-EM

A unified environment for DNN-based automated segmentation of neuronal EM images

Japanese version here

A unified environment for DNN-based automated segmentation of neuronal EM images

Table of contents

• Introduction
• System requirements
• Installation
• Authors
• Reference
• License
• Acknowledgments

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Check the following pages after installation.

• How to use: Folder manager
• How to use: Dojo proofreader
• How to use: 3D annotator
• How to use: 2D CNN
• How to use: 3D FFN
• How to use: 2D and 3D filters
• Example workflow1: Mitochondria segmentation using 2D CNN
• Example workflow2: Membrane segmentation using 3D FFN
• How to make a plugin

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Introduction

Recent years have seen a rapid expansion in the field of micro-connectomics, which targets 3D reconstruction of neuronal networks from a stack of 2D electron microscopic (EM). The spatial scale of the 3D reconstruction grows rapidly over 1 mm3, thank to deep neural networks (DNN) that enable automated neuronal segmentation. Advanced research teams have developed their own pipelines for the DNN-based large-scale segmentation (Informatics 2017, 4:3, 29). Those pipelines are typically a series of client-server software for alignment, segmentation, proofreading, etc., each of which requires specific PC configuration. Because of such complexity, it is difficult even for computer experts to use them, and impossible for experimentalists. This makes a serious divide between the advanced and general experimental laboratories. To bridge this divide, we are now trying to unify pieces of software for automated EM segmentation.

1. We built a desktop version of the proofreading software Dojo (IEEE Trans. Vis. Comput. Graph. 20, 2466–2475).
2. We merged it with a DNN framework: Google Tensorflow/tensorboard.
3. We then incorporated four types of DNN-based segmentation programs: 2D U-Net, ResNet, DenseNet, and HighwayNet. (https://github.com/tbullmann/imagetranslation-tensorflow) and flood-filling networks (https://github.com/google/ffn).
4. A 3D annotator was equipped for visual inspection and annotation.
5. 2D/3D filtration functions were equipped for pre/postprocessing of the segmented images.

Multiple users can simultaneously use it through web browsers. The goal is to develop a unified software environment for ground truth preparation, DNN-based segmentation, pre/postprocessing, proofreading, annotation, and visualization.

System requirements

Operating system: Microsoft Windows 10 (64 bit) or Linux (Ubuntu 18.04).

Recommendation: High-performance NVIDIA graphics card whose GPU has over 3.5 compute capability (e.g., GeForce GTX1080ti, RTX2080ti, and RTX3090).

• https://developer.nvidia.com/cuda-gpus

Installation

We provide standalone versions (pyinstaller version) and Python source codes.

Pyinstaller version (Microsoft Windows 10 only)

1. Download one of the following two versions, and unzip it:

• Version 0.92 (2021/09/17):

  • CPU & GPU version (Ver0.92; 2,166 MB)

• Release summary:

  • Compatibility with both tensorflow1.X and 2.X. UNI-EM thus works on NVIDIA Ampere GPUs (RTX30X0, etc) that require >TF2.4/cuda11.0.
    • Caution: FFN model in TF2 is not identical to that in TF1. Trained model using TF1 cannot be used for further training or inference in TF2.
  • Revision of documents for FFNs.
  • Bug fix (Tensorboard, 2D/3D watersheds, Filetypes of 2D CNN inference, etc).

2. Download one of sample EM/segmentation dojo folders from the following link, and unzip it:

   • https://www.dropbox.com/s/pxds28wdckmnpe8/ac3x75.zip?dl=0
   • https://www.dropbox.com/s/6nvu8o6she6rx9v/ISBI_Dojo.zip?dl=0

3. Click the link "main.exe" in [UNI-EM] to launch the control panel.

4. Select Dojo → Open Dojo Folder from the dropdown menu, and specify the folder of the sample EM/segmentation dojo files. The proofreading software Dojo will be launched.

• Update the dirver of NVIDIA GPU if you see the following error.

  • tensorflow.python.framework.errors_impl.InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
  • https://www.nvidia.com/Download/index.aspx

• Caution: FFN model in Tensorflow 1 (TF1) is not identical to TF2. The trained model using TF1 cannot be used for further training or inference in TF2.

• In the process of traning, HU sees the following warning, and has not found out how to suppress it. I ask someone for help.

  • WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.

Python version

1. Install Python 3.6- in a Microsoft Windows PC (64 bit) or Linux PC (Ubuntu 18.04 confirmed).

2. Install "cuda 11.0 and cuDNN 8.0.4 for Tensorflow 2.4.1", or "cuda 11.2.2 and cuDNN 8.1.1 for Tensorflow 2.5.0" if your PC has a NVIDIA-GPU.

   • https://www.tensorflow.org/install/source
   • https://www.tensorflow.org/install/source_windows

3. Download source code from the github site:

   • git clone https://github.com/urakubo/UNI-EM.git

4. Install the following modules of Python: Tensorflow-gpu, PyQt5, openCV3, pypng, tornado, pillow, libtiff, mahotas, h5py, lxml, numpy, scipy, scikit-image, pypiwin32, numpy-stl. Use the command "requirements-[os]-.txt". Use the following "apt" commands to install opencv and pyqt5 if you use Ubuntu/Linux:

   • sudo apt install python3-dev python3-pip
   • sudo apt install python3-opencv
   • sudo apt install python3-pyqt5
   • sudo apt install python3-pyqt5.qtwebengine

5. Download one of sample EM/segmentation dojo folders from the following link, and unzip it:

   • https://www.dropbox.com/s/pxds28wdckmnpe8/ac3x75.zip?dl=0
   • https://www.dropbox.com/s/6nvu8o6she6rx9v/ISBI_Dojo.zip?dl=0

6. Execute "python main.py" in the [UNI-EM] folder. The control panel will appear.

7. Select Dojo → Open Dojo Folder from the dropdown menu, and specify the sample EM/segmentation dojo folder. The proofreading software Dojo will be launched.

• Update the dirver of NVIDIA GPU if you see the following error.

  • tensorflow.python.framework.errors_impl.InternalError: cudaGetDevice() failed. Status: CUDA driver version is insufficient for CUDA runtime version
  • https://www.nvidia.com/Download/index.aspx

• Caution: FFN model in Tensorflow 1 (TF1) is not identical to TF2. The trained model using TF1 cannot be used for further training or inference in TF2.

• In the process of traning, HU sees the following warning, and has not found out how to suppress it. I ask someone for help.

  • WARNING:tensorflow:It seems that global step (tf.train.get_global_step) has not been increased. Current value (could be stable): 0 vs previous value: 0. You could increase the global step by passing tf.train.get_global_step() to Optimizer.apply_gradients or Optimizer.minimize.

Authors

• Hidetoshi Urakubo - Initial work -
• Ryoji Miyamoto - Frontend GUI -
• Torsten Bullmann - 2D convolutional neural networks -
• Naoki Tamura - Deployment using pyinstaller -
• Ryoya Kamikawa - GUI of classic image filters -

Reference

Urakubo, H., Bullmann, T., Kubota, Y., Oba, S., Ishii, S., UNI-EM: An Environment for Deep Neural Network-Based Automated Segmentation of Neuronal Electron Microscopic Images. Scientific Reports 9, 19413 (2019) doi:10.1038/s41598-019-55431-0

License

This project is licensed under the GNU General Public License (GPLv3) - see the LICENSE file for details.

Acknowledgments

This software relies on the following excellent free yet copyrighted software packages. We obey policies of those software packages.

- Flood-filling networks (Apache License 2.0)
- Imagetranslation-tensorflow (MIT)
- Tensorflow, Tensorboard (Apache License 2.0)
- PyQT5 (GPLv3)
- Rhoana Dojo (MIT)
- Open CV3 (3-clause BSD License, https://opencv.org/license.html)
- Scikit image (http://scikit-image.org/docs/dev/license.html)
- Three.js (MIT)
- Tabulator (MIT) https://github.com/olifolkerd/tabulator/blob/master/LICENSE
- Bootstrap (MIT) https://getbootstrap.com/docs/4.0/about/license/

Hidetoshi Urakubo 2019/2/1