Deep Learning methods for the segmentation of Tumour Spheroids
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
For example, for installing it in Ubuntu use:
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.
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:
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
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 gui --help,
deep-tumour-spheroid image --help,
deep-tumour-spheroid folder --help for a detailed help.
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.
Convert ROI to Mask
💼 Linkedin David Lacalle Castillo
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