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cryoCARE-0.2.2
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مشاهده بیشترتوضیحات
cryoCARE is a deep learning approach for cryo-TEM tomogram denoising.
| ویژگی | مقدار |
|---|---|
| سیستم عامل | - |
| نام فایل | cryoCARE-0.2.2 |
| نام | cryoCARE |
| نسخه کتابخانه | 0.2.2 |
| نگهدارنده | [] |
| ایمیل نگهدارنده | [] |
| نویسنده | Tim-Oliver Buchholz, Thorsten Wagner |
| ایمیل نویسنده | tim-oliver.buchholz@fmi.ch, thorsten.wagner@mpi-dortmund.mpg.de |
| آدرس صفحه اصلی | https://github.com/juglab/cryoCARE_pip |
| آدرس اینترنتی | https://pypi.org/project/cryoCARE/ |
| مجوز | - |
# cryoCARE
This package is a memory efficient implementation of [cryoCARE](https://github.com/juglab/cryoCARE_T2T).
This setup trains a denoising U-Net for tomographic reconstruction according to the [Noise2Noise](https://arxiv.org/pdf/1803.04189.pdf) training paradigm.
Therefore the user has to provide two tomograms of the same sample.
The simplest way to achieve this is with direct-detector movie-frames.
You can use Warp to generate two reconstructed tomograms based on the even/odd frames. Alternatively, the movie-frames can be split in two halves (e.g. with MotionCor2 `-SplitSum 1` or with IMOD `alignframes -debug 10000`) from which two identical, up to random noise, tomograms can be reconstructed.
These two (even and odd) tomograms can be used as input to this cryoCARE implementation.
## Changelog
### Version 0.2
* `cyroCARE_train` produces a compressed and more portable model. This model can be copied and shared with others without relying on a certain folder structure.
* `cryoCARE_predict` supports to predict multiple tomograms in one run. Streamlined configuration with respect to the changes of `cryoCARE_train`.
* Streamlined installation instructions
* CUDA 11 support
* Minor changes/ fixed couple of bugs:
* Proper padding of tomograms to avoid black frames in the denoised tomograms
* Fix computation of validation cut off for small tomograms
* Fix `cryoCARE_predict` if no tiling happens
## Installation
__Note:__ We assume that you have [miniconda](https://docs.conda.io/en/latest/miniconda.html) installed.
First you need to create a conda environment.
### For CUDA 11:
```
conda create -n cryocare_11 python=3.8 cudatoolkit=11.0 cudnn=8.0 -c conda-forge
conda activate cryocare_11
pip install tensorflow==2.4
pip install cryoCARE
```
### For CUDA 10:
```
conda create -n cryocare -c conda-forge -c anaconda python=3 keras-gpu=2.3.1
conda activate cryocare
pip install cryoCARE
```
## Manual
cryoCARE uses `.json` configuration files and is run in three steps:
### 1. Prepare Training Data
To prepare the training data we have to provide all tomograms on which we want to train.
Create an empty file called `train_data_config.json`, copy-paste the following template and fill it in.
```
{
"even": [
"/path/to/even.rec"
],
"odd": [
"/path/to/odd.rec"
],
"patch_shape": [
72,
72,
72
],
"num_slices": 1200,
"split": 0.9,
"tilt_axis": "Y",
"n_normalization_samples": 500,
"path": "./"
}
```
#### Parameters:
* `"even"`: List of all even tomograms.
* `"odd"`: List of all odd tomograms. Note the order has to be the same as in `"even"`.
* `"patch_shape"`: Size of the sub-volumes used for training. Should not be smaller than `64, 64, 64`.
* `"num_slices"`: Number of sub-volumes extracted per tomograms.
* `"tilt_axis"`: Tilt-axis of the tomograms. We split the tomogram along this axis to extract train- and validation data separately.
* `"n_normalization_samples"`: Number of sub-volumes extracted per tomograms, which are used to compute `mean` and `standard deviation` for normalization.
* `"path"`: The training and validation data are saved here.
#### Run Training Data Preparation:
After installation of the package we have access to built in Python-scripts which we can call.
To run the training data preparation we run the following command:
`cryoCARE_extract_train_data.py --conf train_data_config.json`
### 2. Training
Create an empty file called `train_config.json`, copy-paste the following template and fill it in.
```
{
"train_data": "./",
"epochs": 100,
"steps_per_epoch": 200,
"batch_size": 16,
"unet_kern_size": 3,
"unet_n_depth": 3,
"unet_n_first": 16,
"learning_rate": 0.0004,
"model_name": "model_name",
"path": "./",
"gpu_id": 0
}
```
#### Parameters:
* `"train_data"`: Path to the directory containing the train- and validation data. This should be the same as the `"path"` from above.
* `"epochs"`: Number of epochs used to train the network.
* `"steps_per_epoch"`: Number of gradient steps performed per epoch.
* `"batch_size"`: Used training batch size.
* `"unet_kern_size"`: Convolution kernel size of the U-Net. Has to be an odd number.
* `"unet_n_depth"`: Depth of the U-Net.
* `"unet_n_first"`: Number of initial feature channels.
* `"learning_rate"`: Learning rate of the model training.
* `"model_name"`: Name of the model.
* `"path"`: Output path for the model.
* `"gpu_id"`: This is optional. Provide the GPU ID(s) of the GPUs you wish to use.
#### Run Training:
To run the training we run the following command:
`cryoCARE_train.py --conf train_config.json`
You will find a `.tar.gz` file in the directory you specified as `path`. This your model an will be used in the next step.
### 3. Prediction
Create an empty file called `predict_config.json`, copy-paste the following template and fill it in.
```
{
"path": "path/to/your/model/model_name.tar.gz",
"even": "/path/to/even.rec",
"odd": "/path/to/odd.rec",
"n_tiles": [1,1,1],
"output": "denoised.rec",
"overwrite": False,
"gpu_id": 0
}
```
#### Parameters:
* `"path"`: Path to your model file.
* `"even"`: Path to directory with even tomograms or a specific even tomogram or a list of specific even tomograms.
* `"odd"`: Path to directory with odd tomograms or a specific odd tomogram or a list of specific odd tomograms in the same order as the even tomograms.
* `"n_tiles"`: Initial tiles per dimension. Gets increased if the tiles do not fit on the GPU.
* `"output"`: Path where the denoised tomograms will be written.
* `"overwrite"`: Allow previous files to be overwritten.
* `"gpu_id"`: This is optional. Provide the GPU ID(s) of the GPUs you wish to use.
#### Run Prediction:
To run the training we run the following command:
`cryoCARE_predict.py --conf predict_config.json`
## How to Cite
```
@inproceedings{buchholz2019cryo,
title={Cryo-CARE: content-aware image restoration for cryo-transmission electron microscopy data},
author={Buchholz, Tim-Oliver and Jordan, Mareike and Pigino, Gaia and Jug, Florian},
booktitle={2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)},
pages={502--506},
year={2019},
organization={IEEE}
}
@article{buchholz2019content,
title={Content-aware image restoration for electron microscopy.},
author={Buchholz, Tim-Oliver and Krull, Alexander and Shahidi, R{\'e}za and Pigino, Gaia and J{\'e}kely, G{\'a}sp{\'a}r and Jug, Florian},
journal={Methods in cell biology},
volume={152},
pages={277--289},
year={2019}
}
```
زبان مورد نیاز
| مقدار | نام |
|---|---|
| >=3.8 | Python |
نحوه نصب
نصب پکیج whl cryoCARE-0.2.2:
pip install cryoCARE-0.2.2.whl
نصب پکیج tar.gz cryoCARE-0.2.2:
pip install cryoCARE-0.2.2.tar.gz