Metadata-Version: 2.1
Name: hypast
Version: 0.0.5
Summary: Hypothalamus Automatic Segmentation Tool
Home-page: https://github.com/MICLab-Unicamp/HypAST
Author: Livia Rodrigues
Author-email: l180545@dac.unicamp.br
License: UNKNOWN
Description: # HypAST - Hypothalamus Automatic Segmentation Tool
        
        On this package you will find a trained model for hypothalamus segmentation on T1 MRI images and a trainable class, in case you wish to use your own data.
        
        This tool is not suitable for clinical purposes.
        
        ## INSTALLATION
        
                pip install hypast==0.0.3
        
        **HypAST** requires **Python 3.7** and **PyTorch 1.9** 
        
        To install PyTorch:
        
                # CUDA 10.2
                conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=10.2 -c pytorch
                # CUDA 11.3
                install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cudatoolkit=11.3 -c pytorch -c conda-forge
                # CPU Only
                conda install pytorch==1.9.0 torchvision==0.10.0 torchaudio==0.9.0 cpuonly -c pytorch
        
        ## GETTING STARTED
        
        **HypAST** works on .nii or .nii.gz input files for images and .nii, .nii.gz or .npy for annotations. 
        Using **HypAST** you will be able to predict hypothalamus segmentation using our model or to train with your own data.
        
        See more bellow:
        
        #### Trainer
        
        With **Trainer** you can train the model using your own data. 
        
        Example:
        
                import hypast as hyp
                train = hyp.Trainer(train_path, chkp_path, val_path, maxep=200, accum=16, weight=[1,4], lr=5e-3, bs=8)
                train.trainer()
        
        - Input:
        
            - train_path: path to h5py train set
            - chkp_path: checkpoint path
            - val_path: path to h5py val set
            - maxep: Maximum # of epochs in training (defaul = 200)
            - accum: Batch accumulation (defaul = 16)
            - weight: Cross Entropy Weight (defaul = [1,4])
            - lr: Learning Rate (defaul = 5e-3)
            - bs: Batch Size (defaul = 8)
        
        - Output:
            - Checkpoint file on defined path
        
        
        #### CreateHDF5
        
        
        To facilitate training using **HypAST**, CreateHDF5 will adjust your data for you.
        
        Example:
        
                import hypast as hyp
                hyp.CreateHDF5(list_data, list_labels, out_path)
                create.create_links() 
        
        - Input:
        
            - list_data: List containing paths of .nii or .nii.gz images
            - list_labels: List containing paths of labels (.nii, .nii.gz or .npy)
            - out_path: Path were .hdf5 files will be saved
        
        - Output:
        
            - Return train.hdf5 and val.hdf5 on defined path 
        
        #### Predictor
        
        With **HypAST** you can also generate hypothalamus segmentation using our trained model.
        
        Example:
                
                import hypast as hyp
                pred = hyp.Predictor(list_data, out)
                pred.predictor()
        
        - Input:
        
            - list_data: List containing paths of .nii or .nii.gz files to be segmented
            - out: Path were segmentation will be saved
        
        - Output:
        
            - Segmentation files on defined path
        
        ## CONTACT
        
        For more information or suggestions, please contact liviamarodrigues@gmail.com
        
        See more on https://github.com/MICLab-Unicamp/HypAST
        
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: Operating System :: OS Independent
Classifier: License :: OSI Approved :: MIT License
Requires-Python: >3.7
Description-Content-Type: text/markdown
