Digital biology approach for macroscale studies of biofilm growth and biocide effects with electron microscopy
Microbial interactions are one of the major topics of current research due to their great societal relevance. However, our understanding of the processes occurring in biofilms is rather limited. A possible method to partly overcome this problem is the implementation of highly efficient imaging and mapping of these structures. This work proposes a combination of automated scanning electron microscopy (SEM) and a comprehensive software system that uses deep neural networks to perform an in-depth analysis of biofilms. Time-dependent, high-throughput mapping of biofilm electron microscopy images was achieved using deep learning and allowed microscale data analysis of visible to the eye biofilm-covered area (i.e., at the macroscale). For this study, to the best of our knowledge, the first matrix and cell-annotated biofilm segmentation dataset was prepared. We show that the presented approach can be used to process statistical data investigation of biofilm samples in a volume, where automation is essential (>70 000 separate bacterial cells studied; >1000 times faster than regular manual analysis).
Here, The code for the article "Digital biology approach for macroscale studies of biofilm growth and biocide effects" is presented.
- If you want to train your own model, you have to run ./train_network.py
Example:
python train_network.py --path data/cell_data_3A.pkl --arch_name UNet --encoder_name resnet34 --batch_size 5 --crop_size 896 --optimizer_name Adam --savelogdir test_model_training --elastic_transform_size 5P.S. You can change augmentations during training in network_training/augmentations.py
- If you want to check test examples, you have to run test_examples.py
Example:
python test_examples.py --split test --report_images_output_dir data/test_sample_model_preds --checkpoint_path final_models/final_model.ckpt-
Examples of using the model for SEM images segmentation are shown in img_analysis_at_scale/large_scale_segmentation.py, kinetic_modeling/seg_model_for_kinetic_modeling.py and mapping-4-antibiotics-impact/seg_model_for_antibiotics.py
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If you want to use cell detection on images, you have previosly to perform image segmentation. After that, you can use function detect_cells() from cell_detection/cell_detection.py
All data and networks, used in the research article, are located here
If you want to run the scripts and jupyter notebooks, it is recommended to locate all necessary files in the ./data/ directory.
It is also recommended to locate all .ckpt files in the ./final_models/ directory.
Kozlov K.S., Boiko D.A., Detusheva E.V., Detushev K.V., Pentsak E.O., Vereshchagin A.N., Ananikov V.P., "Digital biology approach for macroscale studies of biofilm growth and biocide effects with electron microscopy", Digital Discovery, 2023, 2, 1522-1539