Deep Learning methods for the segmentation of Tumour Spheroids
Project 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:
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
Transform Image
Convert ROI to Mask
Generate Dataset
📩 Contact
📧 dvdlacallecastillo@gmail.com
💼 Linkedin David Lacalle Castillo
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