Metadata-Version: 2.1
Name: Deep-Tumour-Spheroid
Version: 1.0.0rc1
Summary: Deep Learning methods for the segmentation of Tumour Spheroids
Home-page: https://github.com/WaterKnight1998/Deep-Tumour-Spheroid
Author: David Lacalle Castillo
Author-email: dvdlacallecastillo@gmail.com
Maintainer: David Lacalle Castillo
Maintainer-email: dvdlacallecastillo@gmail.com
License: GNU General Public License v3 (GPLv3)
Description: # Deep-Tumour-Spheroid
        
        This package contains several commands and utilities to easily use Semantic Segmentation models in tumor spheroids detection, specifically Glioblastoma Multiforme Tumors (GBM).
        
        ## 🚀 Getting Started
        
        To start using this package, install it using `pip`:
        
        For example, for installing it in Ubuntu use:
        ```bash
        pip3 install Deep-Tumour-Spheroid
        ```
        
        It is recommended to install it globally and not inside virtual environments.
        Have been tested in Windows, Linux and MacOS.
        
        ## 👩‍💻 Usage
        This package makes easier the use of the best trained model. For that purpose you have available 2 commands:
        * `deep-tumour-spheroid image <inputImagePath> <outputFolder>` This method predict over an image. Supported types are: `.jpg`, `.png`, `.nd2`, `.tif` y `.tiff`.
        * `deep-tumour-spheroid folder <inputFolder> <outputFolder>` This method predict in all the images of a folder.
        
        You can use `deep-tumour-spheroid` or it's two abbreviations `dts` or `deep-tumour`.
        
        In addition, you can use the GUI developed for preparing the dataset. For that purpose run: `deep-tumour-spheroid gui`. More information of the utilities in the next section.
        
        You can also execute `deep-tumour-spheroid --help`, `deep-tumour-spheroid gui --help`, `deep-tumour-spheroid image --help`, `deep-tumour-spheroid folder --help` for a detailed help.
        
        ## 💻 GUI
        
        This GUI contains 4 different utilities: predict, convert ".nd2" and ".tiff" 8 bits unsigned to ".png", transform ".roi" into a ".png" Mask and generating the Dataset.
        
        ### Predict
        ![Predict](https://raw.githubusercontent.com/WaterKnight1998/Deep-Tumour-Spheroid/feature/python-package/python-package/readme_images/predict_tumour.png)
        
        ### Transform Image
        ![Transform Image](https://raw.githubusercontent.com/WaterKnight1998/Deep-Tumour-Spheroid/feature/python-package/python-package/readme_images/transform_image.png)
        
        ### Convert ROI to Mask
        ![Convert ROI to Mask](https://raw.githubusercontent.com/WaterKnight1998/Deep-Tumour-Spheroid/feature/python-package/python-package/readme_images/convert_roi_to_mask.png)
        
        ### Generate Dataset
        ![Generate Dataset](https://raw.githubusercontent.com/WaterKnight1998/Deep-Tumour-Spheroid/feature/python-package/python-package/readme_images/generate_dataset.png)
        
        
        ## 📩 Contact
        📧 dvdlacallecastillo@gmail.com
        
        💼 Linkedin [David Lacalle Castillo](https://es.linkedin.com/in/david-lacalle-castillo-5b6280173)
        
Keywords: gbm brain tumour spheroid spheroids deep learning semantic segmentation pytorch
Platform: UNKNOWN
Classifier: Development Status :: 5 - Production/Stable
Classifier: Intended Audience :: Science/Research
Classifier: Intended Audience :: Healthcare Industry
Classifier: Intended Audience :: Developers
Classifier: Topic :: Scientific/Engineering :: Bio-Informatics
Classifier: Topic :: Scientific/Engineering :: Medical Science Apps.
Classifier: Topic :: Scientific/Engineering :: Image Recognition
Classifier: Topic :: Scientific/Engineering :: Artificial Intelligence
Classifier: License :: OSI Approved :: GNU General Public License v3 (GPLv3)
Classifier: Programming Language :: Python :: 3.6
Classifier: Programming Language :: Python :: 3.7
Classifier: Programming Language :: Python :: 3.8
Description-Content-Type: text/markdown
