Automatic Fetal Brain Quality Assessment

Developed by Iván Legorreta

Contact info: ilegorreta@outlook.com

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The aim of this project was to develop a Quality Assessment tool for fetal brain MRIs, which is able to score each volume through a deep learning regression model. Developed using Python3 and Keras/Tensorflow framework.

Our network architecture consists of a non-linear configuration, known as Residual Network (ResNet) architecture:

Given that we are dealing with an unbalanced distribution regarding input dataset, we applied different weights to each input class to compensate for the imbalance in the training sample.

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Requirements

• Linux environment
• Python3
• Conda/Anaconda setup

Installation

1. Create the environment from the conda_environment.yml file:

  conda env create -f conda_environment.yml

The first line of the yml file sets the new environment's name.

2. Activate the new environment: conda activate myenv

3. Verify that the new environment was installed correctly:

  conda env list

You can also use conda info --envs

For more information, visit Conda documentation.