| Name | tirfm-deepfinder JSON |
| Version |
0.2.1
JSON |
| download |
| home_page | None |
| Summary | ExoDeepFinder is an original deep learning approach to localize macromolecules in cryo electron tomography images. The method is based on image segmentation using a 3D convolutional neural network. |
| upload_time | 2024-06-11 14:38:00 |
| maintainer | None |
| docs_url | None |
| author | E. Moebel |
| requires_python | >=3.9 |
| license | GPL-3.0 |
| keywords |
|
| VCS |
|
| bugtrack_url |
|
| requirements |
No requirements were recorded.
|
| Travis-CI |
No Travis.
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| coveralls test coverage |
No coveralls.
|
# ExoDeepFinder
This is a fork of [DeepFinder](https://github.com/deep-finder/cryoet-deepfinder) customized for use in TIRF microscopy, designed for detecting exocytosis events.
## Contents
- [System requirements](##System requirements)
- [Installation guide](##Installation guide)
- [Instructions for use](##Instructions for use)
- [Documentation](https://cryoet-deepfinder.readthedocs.io/en/latest/)
- [Google group](https://groups.google.com/g/deepfinder)
## System requirements
**Deep Finder** has been implemented using **Python 3** and is based on the **Tensorflow** package. It has been tested on Linux (Debian 10), and should also work on Mac OSX as well as Windows.
### Package dependencies
Deep Finder depends on following packages. The package versions for which our software has been tested are displayed in brackets:
```
tensorflow (2.11.1)
lxml (4.9.3)
mrcfile (1.4.3)
scikit-learn (1.3.2)
scikit-image (0.22.0)
matplotlib (3.8.1)
PyQt5 (5.13.2)
pyqtgraph (0.13.3 )
openpyxl (3.1.2)
pycm (4.0)
```
## Installation guide
Before installation, you need a python environment on your machine.
If this is not the case, we advise installing [Miniconda](https://docs.conda.io/en/latest/miniconda.html).
(Optional) Before installation, we recommend first creating a virtual environment that will contain your DeepFinder installation:
```
conda create --name dfinder python=3.9
conda activate dfinder
```
Now, you can install DeepFinder with pip:
```
pip install -e /path/to/tirfm-deepfinder
```
Also, in order for Tensorflow to work with your Nvidia GPU, you need to install CUDA.
An alternative could be to install the python packages `cudatoolkit` and `cudnn`.
Once these steps have been achieved, the user should be able to run DeepFinder.
## Instructions for use
Instructions for using Deep Finder are contained in folder examples/. The scripts contain comments on how the tool should be used.
### Annotation
The first step is to annotate the exocytose events in the movies with the [napari-deepfinder](https://github.com/deep-finder/napari-deepfinder) plugin.
Follow the install instructions, and open napari.
In the menu, choose `Plugins > Napari DeepFinder > Annotation` to open the annotation tools.
Open a training image.
Create a new points layer, and name it `exo_1` (or any name ending with `_1`, since we want to annotate with the class 1).
You can use the Orthoslice view to easily navigate in the volume, by using the `Plugins > Napari DeepFinder > Orthoslice view` menu.
Scroll in the image until you find and exocytose event.
Click the "Add point" or "Add points" button, then click on the exocytose event to annotate it.
Save your annotations to xml by choosing the `File > Save selected layer(s)...` menu, or by using ctrl+S (command+S on a mac), **and choose the *Napadi DeepFinder (\*.xml)* format**, name the output file with the `_objl.xml` suffix (see the training section for the file naming convention).
### Training
To run the training, you should have a folder containing your data organised in the following way:
```
data/
├── train
│ ├── movie1.h5
│ ├── movie1_objl.xml
│ ├── movie1_target.h5
│ ├── movie2.h5
│ ├── movie2_objl.xml
│ ├── movie2_target.h5
...
└── valid
├── movie3.h5
├── movie3_objl.xml
├── movie3_target.h5
...
```
The targets must contain 2 classes:
- the exocytose events, delineated by experts (class 2),
- the other bright spots, which are not exocytose events, and are detected by the [Atlas](https://gitlab.inria.fr/serpico/atlas) spot detector (class 1).
Once the experts have annotated the training and validation images by creating the `objl.xml` files describing the exocytose events, the corresponding segmentations must be generated with the `step1_generate_target.py` script (`cd examples/training/`, then `python step1_generate_target.py`). This will create a segmentation from all events, each with the predefined exocytose shape.
Then, the other non-exocytose events must be detected with [Atlas](https://gitlab.inria.fr/serpico/atlas). The installation instructions are detailed in the repository.
Once Atlas is installed, you can generate the bright spots segmentations and convert them to the h5 format with the following commands:
- `python compute_segmentations.py -a build/atlas -d path/to/dataset/ -o path/to/output/segmentations/`
- `python convert_tiff_to_h5.py -s path/to/output/segmentations/ -o path/to/output/segmentations_h5/`
Use `python compute_segmentations.py --help` and `python convert_tiff_to_h5.py --help` for more information about those tools.
Then, the experts segmentations must be merged with the Atlas detections with the `step2_merge_atlas_targets.py` script.
Finally, the training can be launched with `step3_launch_training.py`.
### Prediction
Predictions can be generated with the `step1_launch_segment.py` script.
This will generate binary segmentations ; the `step2_launch_clustering.py` script can convert them to distinct spots, so that each event gets a unique label.
Finally, the results can be evaluated with the `step3_launch_evaluation.py` (which will make use of the `evaluate.py` tool).
#### Using the GUI
The [napari-deepfinder](https://github.com/deep-finder/napari-deepfinder) plugin can be used to perform perictions.
Open the image you want to segment in napari.
In the menu, choose `Plugins > Napari DeepFinder > Segment` to open the segmentation tools.
Choose the image layer you want to segment.
Select the `examples/analyze/in/net_weights_FINAL.h5` net weights ; or the path of the model weights you want to use for the segmentation.
Use 3 for the number of class, and 160 for the patch size.
Choose an output image name (with the .h5 extension), then launch the segmentation.
Raw data
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"description": "# ExoDeepFinder\n\nThis is a fork of [DeepFinder](https://github.com/deep-finder/cryoet-deepfinder) customized for use in TIRF microscopy, designed for detecting exocytosis events. \n\n## Contents\n- [System requirements](##System requirements)\n- [Installation guide](##Installation guide)\n- [Instructions for use](##Instructions for use)\n- [Documentation](https://cryoet-deepfinder.readthedocs.io/en/latest/)\n- [Google group](https://groups.google.com/g/deepfinder)\n\n## System requirements\n**Deep Finder** has been implemented using **Python 3** and is based on the **Tensorflow** package. It has been tested on Linux (Debian 10), and should also work on Mac OSX as well as Windows.\n\n### Package dependencies\nDeep Finder depends on following packages. The package versions for which our software has been tested are displayed in brackets:\n```\ntensorflow (2.11.1)\nlxml (4.9.3)\nmrcfile (1.4.3)\nscikit-learn (1.3.2)\nscikit-image (0.22.0)\nmatplotlib (3.8.1)\nPyQt5 (5.13.2)\npyqtgraph (0.13.3 )\nopenpyxl (3.1.2)\npycm (4.0)\n```\n\n## Installation guide\nBefore installation, you need a python environment on your machine. \nIf this is not the case, we advise installing [Miniconda](https://docs.conda.io/en/latest/miniconda.html).\n\n(Optional) Before installation, we recommend first creating a virtual environment that will contain your DeepFinder installation:\n```\nconda create --name dfinder python=3.9\nconda activate dfinder\n```\n\nNow, you can install DeepFinder with pip:\n```\npip install -e /path/to/tirfm-deepfinder\n```\n\nAlso, in order for Tensorflow to work with your Nvidia GPU, you need to install CUDA. \nAn alternative could be to install the python packages `cudatoolkit` and `cudnn`.\nOnce these steps have been achieved, the user should be able to run DeepFinder.\n\n## Instructions for use\n\nInstructions for using Deep Finder are contained in folder examples/. The scripts contain comments on how the tool should be used.\n\n### Annotation\n\nThe first step is to annotate the exocytose events in the movies with the [napari-deepfinder](https://github.com/deep-finder/napari-deepfinder) plugin.\nFollow the install instructions, and open napari.\nIn the menu, choose `Plugins > Napari DeepFinder > Annotation` to open the annotation tools.\nOpen a training image.\nCreate a new points layer, and name it `exo_1` (or any name ending with `_1`, since we want to annotate with the class 1).\nYou can use the Orthoslice view to easily navigate in the volume, by using the `Plugins > Napari DeepFinder > Orthoslice view` menu.\nScroll in the image until you find and exocytose event.\nClick the \"Add point\" or \"Add points\" button, then click on the exocytose event to annotate it.\nSave your annotations to xml by choosing the `File > Save selected layer(s)...` menu, or by using ctrl+S (command+S on a mac), **and choose the *Napadi DeepFinder (\\*.xml)* format**, name the output file with the `_objl.xml` suffix (see the training section for the file naming convention).\n\n### Training\n\nTo run the training, you should have a folder containing your data organised in the following way:\n\n```\ndata/\n\u251c\u2500\u2500 train\n\u2502 \u251c\u2500\u2500 movie1.h5\n\u2502 \u251c\u2500\u2500 movie1_objl.xml\n\u2502 \u251c\u2500\u2500 movie1_target.h5\n\u2502 \u251c\u2500\u2500 movie2.h5\n\u2502 \u251c\u2500\u2500 movie2_objl.xml\n\u2502 \u251c\u2500\u2500 movie2_target.h5\n...\n\u2514\u2500\u2500 valid\n \u251c\u2500\u2500 movie3.h5\n \u251c\u2500\u2500 movie3_objl.xml\n \u251c\u2500\u2500 movie3_target.h5\n...\n```\n\nThe targets must contain 2 classes:\n- the exocytose events, delineated by experts (class 2),\n- the other bright spots, which are not exocytose events, and are detected by the [Atlas](https://gitlab.inria.fr/serpico/atlas) spot detector (class 1).\n\nOnce the experts have annotated the training and validation images by creating the `objl.xml` files describing the exocytose events, the corresponding segmentations must be generated with the `step1_generate_target.py` script (`cd examples/training/`, then `python step1_generate_target.py`). This will create a segmentation from all events, each with the predefined exocytose shape.\n\nThen, the other non-exocytose events must be detected with [Atlas](https://gitlab.inria.fr/serpico/atlas). The installation instructions are detailed in the repository.\n\nOnce Atlas is installed, you can generate the bright spots segmentations and convert them to the h5 format with the following commands:\n- `python compute_segmentations.py -a build/atlas -d path/to/dataset/ -o path/to/output/segmentations/`\n- `python convert_tiff_to_h5.py -s path/to/output/segmentations/ -o path/to/output/segmentations_h5/`\n\nUse `python compute_segmentations.py --help` and `python convert_tiff_to_h5.py --help` for more information about those tools.\n\nThen, the experts segmentations must be merged with the Atlas detections with the `step2_merge_atlas_targets.py` script.\n\nFinally, the training can be launched with `step3_launch_training.py`.\n\n### Prediction\n\nPredictions can be generated with the `step1_launch_segment.py` script. \n\nThis will generate binary segmentations ; the `step2_launch_clustering.py` script can convert them to distinct spots, so that each event gets a unique label.\n\nFinally, the results can be evaluated with the `step3_launch_evaluation.py` (which will make use of the `evaluate.py` tool).\n\n#### Using the GUI\n\nThe [napari-deepfinder](https://github.com/deep-finder/napari-deepfinder) plugin can be used to perform perictions.\nOpen the image you want to segment in napari.\nIn the menu, choose `Plugins > Napari DeepFinder > Segment` to open the segmentation tools.\nChoose the image layer you want to segment.\nSelect the `examples/analyze/in/net_weights_FINAL.h5` net weights ; or the path of the model weights you want to use for the segmentation.\nUse 3 for the number of class, and 160 for the patch size.\nChoose an output image name (with the .h5 extension), then launch the segmentation.\n",
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