This repository contains the code and documentation to implement ACME (Automatic Cell Migration Examination):

ACME: Automatic feature extraction for Cell Migration Examination through intravital microscopy imaging,
Miguel Molina-Moreno, Iván González-Díaz, Georgiana Crainiciuc, Miguel Palomino-Segura, Jon Sicilia, Andrés Hidalgo and Fernando Díaz-de-María
Medical Image Analysis, 77, https://doi.org/10.1016/j.media.2022.102358, 2022. 

Behavioral immune landscapes of inflammation,
Georgiana Crainiciuc, Miguel Palomino-Segura, Miguel Molina-Moreno, Jon Sicilia and others.
Nature, vol. 601, no. 7893, 415–421, https://doi.org/10.1038/s41586-021-04263-y, 2022.

This code is partly based on the implementations of U-Net 3D and PhagoSight.

ACME code is released under the GNU GPLv3 License (refer to the LICENSE and COPYING files for details).

If you find ACME useful in your research, please consider citing:

@ARTICLE{acme,
	title = {ACME: Automatic feature extraction for cell migration examination through intravital microscopy imaging},
	journal = {Medical Image Analysis},
	volume = {77},
	pages = {102358},
	year = {2022},
	issn = {1361-8415},
	doi = {https://doi.org/10.1016/j.media.2022.102358},
	url = {https://www.sciencedirect.com/science/article/pii/S1361841522000111},
	author = {Miguel Molina-Moreno and Iván González-Díaz and Jon Sicilia and Georgiana Crainiciuc and Miguel Palomino-Segura and Andrés Hidalgo and Fernando Díaz-de-María}
}

@ARTICLE{behavioral,
	author = {Georgiana Crainiciuc and Miguel Palomino-Segura and Miguel Molina-Moreno and Jon Sicilia and others},
	title = {Behavioral immune landscapes of inflammation},
	journal = {Nature},
	year = {2021},
	volume={601},
	number={7893},
	pages={415-421},
	doi={https://doi.org/10.1038/s41586-021-04263-y},
	ISSN={1476-4687}
}

ACME is implemented to not require any additional modules. MATLAB code has been developed with the R2017b version. The Python code has been tested with Pytorch 1.3.1, torchvision 0.4.2 and CUDA 10.1.

Before executing the Python code, it is necessary to compile the functions in custom_extensions folder, through the setup_roi_align.py script, with the CUDA_HOME environment variable pointing to your CUDA installation.

We describe the pipeline of execution of the code below.

1. First, the matlab/config.m script contains different configurable parameters for the experiment: venule and cell channels, group denomination, voxel size, etc. and the different modules of the system.
2. Once the parameters are set and the sequences are stored in the corresponding folder, the matlab/extract_data.m must be used to store each 3D temporal volume of the 4D sequence (in .mat format) in the directory data/annotation, to facilitate the annotation of the volumes (if you want to test how the segmentation and tracking process is working with your samples you can annotate your own volumes).
3. Now, the matlab/generate_database.m script can be used to generate the database folder with the volumes that will be used as inputs to the 3D Joint Segmentation Module.
4. The data are prepared for the inference process. This is done by the python/detection_inference.py function. The inference results are stored in the database folder.
5. After this step, we return to MATLAB implementation to process the ACME segmentations with the script matlab/process_ACME_segmentations, which accumulates the cell and blood vessel segmentations per capture and performs the 3D three-pass tracking. At this point of the pipeline you can print the images (see the matlab/config.m JPEG variable) in the database/jpgs_results.
6. The next step consists of extracting the instantaneous and dynamic features with the extract_instantaneous_features.m and the extract_dynamic_features.m scripts, respectively.
7. The pipeline of cell detection ends with the matlab/cell_selection_module.m script. From the extracted features, it applies the trained classifier to detect those cells that are well segmented (in terms of the fixed precision level), see matlab/config.m file.
8. Finally, the hierarchical explainability module (hierarchical_explainability.m) detects the behaviors, builds the hierarchy from them and offers the most relevant features for each partition of the hierarchy (there are configurable parameters in config.m). In addition, the python/visualization.py function is able to arrange the data in t-SNE or UMAP plots for a better understanding.

The reported performance of each module for our scenario is presented below:

To start using ACME, download and unzip this repository.

git clone https://github.com/miguel55/ACME

See doc\doc.tex for more details.