EM-stellar

This repository is official implementation of EM-stellar: benchmarking deep learning for electron microscopy image segmentation on Google Colab.

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This notebook represents the implementation of the EM-stellar. Deep learning (DL) methods offer an exciting opportunity to automate the segmentation process by learning from manual annotations of a small sample of EM data. While many DL methods are being rapidly adopted to segment EM data no benchmark analysis has been conducted on these methods to date. We present EM-stellar as a platform that can be used to benchmark the performance of a range of state-of-the-art DL methods on user-provided datasets.

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Papers related to this Notebook:

• EM-stellar: benchmarking deep learning for electron microscopy image segmentation by Afshin Khadangi, Thomas Boudier, Vijay Rajagopal (https://www.biorxiv.org/content/10.1101/2020.07.15.203836v1)

• EM-net: Deep learning for electron microscopy image segmentation by Afshin Khadangi, Thomas Boudier, Vijay Rajagopal (https://www.biorxiv.org/content/10.1101/2020.02.03.933127v1)

Please cite this original paper when using or developing this notebook.

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How to use?!

We have provided the instructions for the usage of this Notebook in the following link:

• Colab Notebook: Walk through the pipeline including data upload and deploying the benchmarking.

Important: Make sure that you create a copy of the following Colab Notebook on your Google Drive before running or making any changes to the notebook

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Images

We have provided a sample dataset as folder "Images", which includes the directories of the ground-truth and predicted segmentation masks. These images can be used as a demo implementation of the EM-stellar. However, you are encouraged to follow the same formatting and structure to try EM-stellar on your own data. For further information, please follow the instructions as highlighted on the notebook.

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Structure of a notebook

The notebook contains two types of cell:

Text cells provide information and can be modified by douple-clicking the cell. You are currently reading the text cell. You can create a new text by clicking + Text.

Code cells contain code and the code can be modfied by selecting the cell. To execute the cell, move your cursor on the [ ]-mark on the left side of the cell (play button appears). Click to execute the cell. After execution is done the animation of play button stops. You can create a new coding cell by clicking + Code.

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Table of contents, Code snippets and Files

On the top left side of the notebook you find three tabs which contain from top to bottom:

Table of contents = contains structure of the notebook. Click the content to move quickly between sections.

Code snippets = contain examples how to code certain tasks. You can ignore this when using this notebook.

Files = contain all available files. After mounting your google drive (see section 1.) you will find your files and folders here.

Remember that all uploaded files are purged after changing the runtime. All files saved in Google Drive will remain. You do not need to use the Mount Drive-button; your Google Drive is connected in section 1.2.

Note: The "sample data" in "Files" contains default files. Do not upload anything in here!

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Making changes to the notebook

You can make a copy of the notebook and save it to your Google Drive. To do this click file -> save a copy in drive.

To edit a cell, double click on the text. This will show you either the source code (in code cells) or the source text (in text cells). You can use the #-mark in code cells to comment out parts of the code. This allows you to keep the original code piece in the cell as a comment.

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EM-stellar: stand-alone version

We have received some good reviews from users who have storage issues on Google Drive or face security/privacy concern over their data. We truly understand such concerns and hence we have provided python script that you can run on your local computer/cluster. The code is provided in run_EM-stellar.py

Prerequisites

This code has been implemented in python. You need to install pyimagej which is a python wrapper for ImageJ.

To install pyimagej:

$ pip install pyimagej

To install scikit-learn:

$ pip install scikit-learn

To install scikit-image:

$ pip install scikit-image

To install pandas:

$ pip install pandas

To install tqdm:

$ pip install tqdm

To install seaborn:

$ pip install seaborn

To install matplotlib:

$ pip install matplotlib

You will also need to install Fiji. If you have already installed Fiji, then we are good!

Help

To get help on using EM-stellar run python run_EM-stellar.py --help

Examples:

Run EM-stellar using the Fiji.app, segmentation results and ground-truth images directories:

• Linux/Mac:

python run_EM-stellar.py --imagej_dir=~/Fiji.app --segmentation_results_dir=~/predicted_masks --ground_truth_binary_masks_dir=~/ground_truth_masks

• Windows:

python run_EM-stellar.py --imagej_dir=C:/Users/Fiji.app --segmentation_results_dir=C:/Users/predicted --ground_truth_binary_masks_dir=C:/Users/ground --windows=true

Outputs:

Running run_EM-stellar.py will generate two files:

• Dataframe_output.html: An html file including the table of the metrics and the performance values
• Heatmap_output.png: An image showing the heatmap of the values of the metrics VS. different methods