Metadata-Version: 2.1
Name: DARTS
Version: 0.0.1.post1
Summary: DenseUnet-based Automatic Rapid brain Segmentation
Home-page: https://github.com/NYUMedML/DARTS
Author: NYUMedML
Author-email: tms598@nyu.edu
License: GNU General Public License v3.0
Description: # DenseUnet-based Automatic Rapid brain Segmentation (DARTS)
        
        ## Package
        * Our Dense UNet model performs automated segmentation of 102 brain regions **within a minute** (on a machine with a single GPU). The DARTS package, which utilizes the best-performing model, can be installed using:
        
        ```
        pip install DARTS
        ```
        
        * Download the pretrained model weights from [here](https://drive.google.com/file/d/1ux3WEU9_n7hEy6gjZWI7Y0WL9P47D9kx/view?usp=sharing) as follows:
        
        ```
        gdown https://drive.google.com/uc?id=1ux3WEU9_n7hEy6gjZWI7Y0WL9P47D9kx
        ```
        
        ## Usage
        
        Segmentation requires the use of a GPU and takes approximately one minute. For optimal results, input image size should be 256x256x256 mm<sup>3</sup> and in one of the following formats: .mgz, .mgh, .nii.gz or .nii. 
        Follow these steps to perform segmentation:
        
        ```
        from DARTS import Segmentation
        seg_obj = Segmentation(model_wts_path='PATH_TO_MODEL_WEIGHTS')
        seg_out = seg_obj.predict(input_image_path='PATH_TO_INPUT_IMAGE', output_dir='PATH_TO_OUTPUT_DIRECTORY')
        ```
        
        Function help:
        ```
        help(Segmentation)
        
         |  Methods defined here:
         |  
         |  __init__(self, model_wts_path='./dense_unet_back2front_finetuned.pth')
         |      Initializes the segmentation object (GPU required)
         |      Parameters
         |      ----------
         |      model_wts_path : Path to pre-trained model (default './dense_unet_back2front_finetuned.pth')
         |  
         |  predict(self, input_image_path, output_dir='./')
         |      Segments the input image
         |      Parameters
         |      ----------
         |      input_image_path: Path to input image (can be .mgh, .mgz, .nii or .nii.gz)
         |      output_dir : Path to save segmentation output and volumes (default './')
         |      
         |      Returns
         |      -------
         |      Segmented mask (np.array)
         |  
        ```
        
        ## Project
        [Here](https://arxiv.org/abs/1911.05567) is the paper describing the project and experiments in detail. We pre-trained our model using the Freesurfer segmentations of 1113 subjects available in the [Human Connectome Project](https://www.humanconnectome.org/study/hcp-young-adult/document/1200-subjects-data-release) dataset and fine-tuned the model using 101 manually labeled brain scans from the [Mindboggle](https://mindboggle.info/data.html) dataset. 
        
        ## Output segmentation
        The output segmentation has 103 labeled segments, which closely resemble the aseg+aparc segmentation protocol of Freesurfer. We also provide region volumes in .csv format. We exclude four brain regions that are not common to a normal brain: White matter and non-white matter hypointentisites, and the left and right frontal and temporal poles. We also exclude left and right bankssts as there is no widely accepted definition of these segments.
        
        ## Contact
        If you have any questions regarding the code, please contact tms598[at]nyu.edu or raise an issue on the github repo.
        
Platform: UNKNOWN
Requires-Python: >=3
Description-Content-Type: text/markdown
