Kernekoncepter
Deep Convolutional Generative Adversarial Networks (DCGANs) can effectively generate realistic synthetic brain MRI image slices by learning the distribution of a clean and prepared dataset of brain MRI scans.
Resumé
The paper explores the use of Deep Convolutional Generative Adversarial Networks (DCGANs) to generate realistic synthetic brain MRI image slices. The key highlights are:
Data Preparation:
A clean and ready-to-use dataset of brain MRI scans in the three anatomical planes (sagittal, coronal, axial) was created by processing and unifying multiple datasets from OpenNeuro.
Custom functions were developed to handle tasks like reading, plotting, and transforming the MRI images to a consistent 256x256 resolution.
DCGAN Architecture:
The generator network was designed to take random noise as input and learn to synthesize realistic 256x256 grayscale MRI image slices.
The discriminator network was built to distinguish between real and generated MRI images, guiding the generator to improve its output through adversarial training.
Binary cross-entropy loss and the Adam optimizer were used for both the generator and discriminator.
Results and Evaluation:
The generated MRI image slices were evaluated qualitatively, showing that the DCGAN was able to capture the structural aspects of the brain, though details were lacking due to the limited training epochs.
Suggestions for further optimization, such as increasing the training duration, trying different GAN variants, and applying data augmentation, were provided to improve the quality of the generated images.
The work contributes to the growing body of research on using deep learning techniques, specifically DCGANs, for synthetic medical image generation, which can be useful for data augmentation, dataset cleaning, and other applications in medical imaging research.
Statistik
The dataset used in this study contained over 18,000 central slices in each of the coronal, sagittal, and axial planes, obtained from multiple brain MRI scans of healthy individuals.
Citater
"The slices that are could be produced possess the capability to enhance datasets, provide data augmentation in the training of deep learning models, as well as a number of functions are made available to make MRI data cleaning easier, and a three ready to use and clean dataset on the major anatomical plans."