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--- a +++ b/monai 0.5.0/README.md @@ -0,0 +1,116 @@ + +# SALMON v.2: Segmentation deep learning ALgorithm based on MONai toolbox +- SALMON is a computational toolbox for segmentation using neural networks (3D patches-based segmentation) +- SALMON is based on NN-UNET and MONAI: PyTorch-based, open-source frameworks for deep learning in healthcare imaging. +(https://github.com/Project-MONAI/MONAI) +(https://github.com/MIC-DKFZ/nnUNet) + +This is my "open-box" version if I want to modify the parameters for some particular task, while the two above are hard-coded. + +******************************************************************************* +## Requirements +Follow the steps in "installation_commands.txt". Installation via Anaconda and creation of a virtual env to download the python libraries and pytorch/cuda. +******************************************************************************* +## Python scripts and their function + +- organize_folder_structure.py: Organize the data in the folder structure (training,validation,testing) for the network. +Labels are resampled and resized to the corresponding image, to avoid array size conflicts. You can set here a new image resolution for the dataset. + +- init.py: List of options used to train the network. + +- check_loader_patches: Shows example of patches fed to the network during the training. + +- networks.py: The architecture available for segmentation is a nn-Unet. + +- train.py: Runs the training + +- predict_single_image.py: It launches the inference on a single input image chosen by the user. +******************************************************************************* +## Usage +### Folders structure: + +Use first "organize_folder_structure.py" to create organize the data. +Modify the input parameters to select the two folders: images and labels folders with the dataset. Set the resolution of the images here before training. + + . + ├── Data_folder + | ├── CT + | | ├── 1.nii + | | ├── 2.nii + | | └── 3.nii + | ├── CT_labels + | | ├── 1.nii + | | ├── 2.nii + | | └── 3.nii + +Data structure after running it: + + . + ├── Data_folder + | ├── CT + | ├── CT_labels + | ├── images + | | ├── train + | | | ├── image1.nii + | | | └── image2.nii + | | └── val + | | | ├── image3.nii + | | | └── image4.nii + | | └── test + | | | ├── image5.nii + | | | └── image6.nii + | ├── labels + | | ├── train + | | | ├── label1.nii + | | | └── label2.nii + | | └── val + | | | ├── label3.nii + | | | └── label4.nii + | | └── test + | | | ├── label5.nii + | | | └── label6.nii + +******************************************************************************* +### Training: +- Modify the "init.py" to set the parameters and start the training/testing on the data. Read the descriptions for each parameter. +- Afterwards launch the "train.py" for training. Tensorboard is available to monitor the training ("runs" folder created) +- Check and modify the train_transforms applied to the images in "train.py" for your specific case. (e.g. In the last update there is a HU windowing for CT images) + +Sample images: the following images show the segmentation of carotid artery from MRI sequence + + + +Sample images: the following images show the multi-label segmentation of prostate transition zone and peripheral zone from MRI sequence + +! + +******************************************************************************* +### Inference: +- Launch "predict_single_image.py" to test the network. Modify the parameters in the parse section to select the path of the weights, images to infer and result. +- You can test the model on a new image, with different size and resolution from the training. The script will resample it before the inference and give you a mask +with same size and resolution of the source image. +******************************************************************************* +### Tips: +- Use and modify "check_loader_patches.py" to check the patches fed during training. +- The "networks.py" calls the nn-Unet, which adapts itself to the input data (resolution and patches size). The script also saves the graph of you network, so you can visualize it. +- Is it possible to add other networks, but for segmentation the U-net architecture is the state of the art. + +### Sample script inference +- The label can be omitted (None) if you segment an unknown image. You have to add the --resolution if you resampled the data during training (look at the argsparse in the code). +```console +python predict_single_image.py --image './Data_folder/image.nii' --label './Data_folder/label.nii' --result './Data_folder/prova.nii' --weights './best_metric_model.pth' +``` +******************************************************************************* +### Multi-channel segmentation: + +The subfolder "multi_label_segmentation_example" include the modified code for multi_labels scenario. +The example segment the prostate (1 channel input) in the transition zone and peripheral zone (2 channels output). +The gif files with some example images are shown above. + +Some note: +- You must add an additional channel for the background. Example: 0 background, 1 prostate, 2 prostate tumor = 3 out channels in total. +- Tensorboard can show you all segmented channels, but for now the metric is the Mean-Dice (of all channels). If you want to evaluate the Dice score for each channel you + have to modify a bit the plot_dice function. I will do it...one day...who knows...maybe not +- The loss is the DiceLoss + CrossEntropy. You can modify it if you want to try others (https://docs.monai.io/en/latest/losses.html#diceloss) + +Check more examples at https://github.com/Project-MONAI/tutorials/blob/master/3d_segmentation/spleen_segmentation_3d.ipynb.