Cell Counting in Fluorescence Microscopy

This repository is the code implementation of the work Morelli, R., Clissa, L., Amici, R. et al. Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet. Sci Rep 11, 22920 ( 2021).

Installation

pip

git clone [email protected]:robomorelli/cell_counting_yellow.git
cd cell_counting_yellow
pip install requirements.txt

docker (TO DO)

-Build the image from dockerfile docker build -t .

-Run the image: docker run --rm -it -p 8888:8888 -v ${PWD}/DATASET:app/DATASET cell

Run initialize a shell session inside the container where it is possible to run the scripts and a jupyter session with the command: jupyter notebook --port=8888 --no-browser --ip=0.0.0.0 --allow-root

connect following the instruction displayed on the terminal

run jupyter directly from docker: docker run -it -p 8888:8888 -v ${PWD}/DATASET:/app/DATASET cell -c "jupyter notebook --port=8888 --no-browser --ip=0.0.0.0 --allow-root"

connect following the instruction displayed on the terminal

Dataset

The data consist of 283 high-resolution pictures (1600x1200 pixels) of mice brain slices acquired through a fluorescence microscope. The final goal is to individuate and count neurons highlighted in the pictures by means of a marker, so to assess the result of a biological experiment. For more information, please refer to [1, 2]. The corresponding ground-truth labels were generated through a hybrid approach involving semi-automatic and manual semantic segmentation. The result consists of black (0) and white (255) images having pixel-level annotations of where the neurons are located. Possible applications include but are not limited to semantic segmentation, object detection and object counting.

After cloning the repository, you can download the data from the Fluorescent Neuronal Cells archive.

Once the zip file of the dataset is downloaded:

1. Create a new folder named DATASET inside the root of the repository
2. Unzip its content into the DATASET folder. Final structure should be:

cell_counting_yellow/
├── DATASET
│   ├── all_images
│   ├── all_masks
│   └── README.md
├── model_results
├── notebooks
└── results

3. Run the script 010_split_data.py to get the exact same train/val VS test split used in the paper. Final structure should be:

cell_counting_yellow/
├── DATASET
│   ├── all_images
│   ├── all_masks
│   ├── README.md
│   ├── test
│   ├── train_val
│   └── test_split_map.csv
├── model_results
├── notebooks
└── results

Customization

All the relative paths and macro-variables are defined in the config.py module. Edit this file for custom configurations.

Usage

Utilities are defined in python scripts used as modules. These are imported in the pipeline scripts (those having names starting with numbers) in order to perform the various steps of the analysis (described in the following).

Pre-processing

• 020_cropper_yellow.py: Crop the images and masks into smaller patches for the training of the network. The default size of the crops will be the same used in the referenced paper (512x512). To make crops with different features check the help description of the script.

• 030_weights_maker.py: Get the weight maps as described in the paper. No additional arguments are required to reproduce the same masks of the paper. Some default parameters (like the sigma factor) can be changed following the help decription.

  python 030_weight_maker.py --normalize True --continue_after_normalization True

• 040_augumentation_yellow.py: Run the augmentation to increase the number of the training-validation images. Use the help to define the augmentation factor both for the images segmented automatically and those segmented manually. Also, it is possible to select or not a strategy for the artifact augmentation. As usual, running without additional arguments reproduce the same augumentation pipeline adopted in the paper. It is worth to remember that the augumentation will produce different images from those used in the paper due to the random nature of the augmentation process.

Training

• 050_dev_model.py: Train a convolutional neural network on the Fluorescent Neuronal Cells dataset. Without additional arguments the ResUnet described in the paper as c-Resunet is trained. To train another architecture use the --model_name argument. Options are: [c_resunet, resunet, unet, small_unet]. Check the help to modify other training parameters like the classes' loss weight: defaults are 1.5 for white pixels (cell class) and 1 for black pixel (background).

• 060_split_file.py: Utility to split the train-val set in subfolder containing 1000 images each. This procedure is suggested only for training on a cluster because the I/O operations on smaller files are preferred in this case.

Evaluation

In order to assess the performance on a model it is possible to exploit the script evaluate_model_test.py

python evaluate_model.py --mode [eval|test|test_code] --threshold [knee|best|grid] --out_folder results/ <model_name>

Note: the model name must be specified without file extension, e.g. c-ResUnet for the weights saved in c-ResUnet.h5

To reproduce the results of the paper run:

# run grid search on the train/val split (full-size images only)
python evaluate_model.py --mode eval --threshold grid --out_folder results/ <model_name>


# evaluate the performance on the test set with the optimal threshold according to the kneedle method
python evaluate_model.py --mode test --threshold knee --out_folder results/ <model_name>

Notebooks

Some useful notebooks are provided to visualize the output of the training and compare different models:

• Compare_models.ipynb: compares the heatmaps and the object detection abilities of multiple models side-by-side
• Visualize_results.ipynb: shows the effect of the post-processing pipeline for a given model
• Visualize Hidden Features.ipynb: plots features computed by intermediate layers
• Export weights.ipynb: transform Keras weights in a state_dict-like format and exports them as pickle files

References

[1] Morelli, R., Clissa, L., Amici, R., Cerri, M., Hitrec, T., Luppi, M., Rinaldi, L., Squarcio, F. and Zoccoli, A. Automating cell counting in fluorescent microscopy through deep learning with c-ResUnet. Sci Rep 11, 22920 (2021). https://doi.org/10.1038/s41598-021-01929-5.

[2] Hitrec, T., Luppi, M., Bastianini, S., Squarcio, F., Berteotti, C., Martire, V.L., Martelli, D., Occhinegro, A., Tupone, D., Zoccoli, G. and Amici, R., 2019. Neural control of fasting-induced torpor in mice. Scientific reports, 9(1), pp.1-12.