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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

Predict

Transform Image

Transform Image

Convert ROI to Mask

Convert ROI to Mask

Generate Dataset

Generate Dataset

📩 Contact

📧 dvdlacallecastillo@gmail.com

💼 Linkedin David Lacalle Castillo

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