Microscope Image Focus Quality Classifier

This repo contains code for using a pre-trained TensorFlow model to classify the quality (e.g. running inference) of image focus in microscope images.

Code for training a new model from a dataset of in-focus only images is included as well.

This is not an official Google product.

See the paper PDF for reference:

Yang, S. J., Berndl, M., Ando, D. M., Barch, M., Narayanaswamy, A. , Christiansen, E., Hoyer, S., Roat, C., Hung, J., Rueden, C. T., Shankar, A., Finkbeiner, S., & and Nelson, P. (2018). Assessing microscope image focus quality with deep learning. BMC Bioinformatics, 19(1).

Setting up a virtual environment

Clone the main branch of this repository.

git clone -b main https://github.com/finkbeiner-lab/microscopeimagequality.git

Navigate to your home directory and create a hidden folder that will contain this (or other) virtualenvs. Create a virtualenv called miq with Python 2.7.

cd
mkdir .virtualenvs
virtualenv --python=/usr/bin/python ~/.virtualenvs/miq

After the virtualenv is set up, activate it.

source ~/.virtualenvs/miq/bin/activate

Install Python dependencies from the requirements.txt file using pip.

pip install -r requirements.txt

Configure your matplotlib backend. Locate the path to the active matplotlibrc through a Python shell.

$ python
>>> import matplotlib
>>> matplotlib.matplotlib_fname()
# /Users/user/.matplotlib/matplotlibrc

If you are installing this on a local machine:

echo 'backend : TkAgg' >> /path/to/matplotlibrc

If you are installing this on a remote machine (the compute nodes):

echo 'backend : Agg' >> /path/to/matplotlibrc

Re-activate the virtualenv.

deactivate
source ~/.virtualenvs/miq/bin/activate

Then continue with the installation as described below!

Getting started

Clone the main branch of this repository. You can skip this step if you already cloned the repository earlier.

git clone -b main https://github.com/finkbeiner-lab/microscopeimagequality.git

Install the package:

cd microscopeimagequality
# Note: To install pip, run "sudo easy_install pip".
# Note: This may need to be run with "sudo pip".
pip install --editable .

Download the model: This downloads the model.ckpt-1000042 checkpoint (a model trained for 1000042 steps) specified in constants.py.

microscopeimagequality download 

or alternatively:

import microscopeimagequality.miq
microscopeimagequality.miq.download_model()

Add path to local repository (e.g. /Users/user/my_repo/microscopeimagequality) to PYTHONPATH environment variable:

export PYTHONPATH="${PYTHONPATH}:/Users/user/my_repo/microscopeimagequality"

Run all tests to make sure everything works. Install any missing packages (e.g. sudo pip install pytest or sudo pip install nose).

pytest --disable-pytest-warnings

You should now be able to run:

microscopeimagequality --help

or directly access the module functions in a jupyter notebook or from your own python module:

from microscopeimagequality import degrade
degrade.degrade(...)

Running inference

Requirements for running inference

• A pre-trained TensorFlow model .ckpt files, downloadable using download instructions above.
• TensorFlow 1.0.0 or higher, numpy, scipy, pypng, PIL, skimage, matplotlib
• Input grayscale 16-bit images, .png of .tif format, all with the same width and height.

How to

(Optional) Confirm that all images are of the same dimension:

 microscopeimagequality validate tests/data/images_for_glob_test/*.tif --width 100 --height 100

Run inference on each image independently.

  microscopeimagequality predict \
  --output tests/output/ \
  tests/data/BBBC006*10.png

Summarize the prediction results across the entire dataset. Output will be in "summary" sub directory.

microscopeimagequality summarize tests/output/miq_result_images/

Training a new model

Requirements

• TensorFlow 1.0.0 or higher, and several other python modules.
• A dataset of high quality, in-focus images (at least 400+), as grayscale 16-bit images, .png of .tif format, all with the same width and height.

How to

1. Generate additional labeled training examples of defocused images using degrade.py.
2. Launch microscopeimagequality fit to train a model.
3. Launch microscopeimagequality evaluate with a held-out test dataset.
4. Use TensorBoard to view training and eval progress (see evaluation.py).
5. When satisfied with model accuracy, save the model.ckpt files for later use.

Example fit:

microscopeimagequality fit \
	--output tests/train_output \
	tests/data/training/0/*.tif \
	tests/data/training/1/*.tif \
	tests/data/training/2/*.tif \
	tests/data/training/3/*.tif \
	tests/data/training/4/*.tif \
	tests/data/training/5/*.tif \
	tests/data/training/6/*.tif \
	tests/data/training/7/*.tif \
	tests/data/training/8/*.tif \
	tests/data/training/9/*.tif \
	tests/data/training/10/*.tif

Example evaluation:

microscopeimagequality evaluate \
	--checkpoint <path_to_model_checkpoint>/model.ckpt-XXXXXXX \
	--output tests/data/output \
	tests/data/training/0/*.tif \
	tests/data/training/1/*.tif \
	tests/data/training/2/*.tif \
	tests/data/training/3/*.tif \
	tests/data/training/4/*.tif \
	tests/data/training/5/*.tif \
	tests/data/training/6/*.tif \
	tests/data/training/7/*.tif \
	tests/data/training/8/*.tif \
	tests/data/training/9/*.tif \
	tests/data/training/10/*.tif