Lesion-Specific Prediction with Discriminator-Based Supervised Guided Attention Module Enabled GANs in Multiple Sclerosis

TL;DR

This paper introduces a novel GAN-based model with lesion-guided attention for predicting future MS lesions in brain MRI, improving accuracy and consistency over existing methods.

Contribution

The study presents a new GAN modification incorporating supervised guided attention and dilated convolutions for lesion-specific MRI prediction in MS.

Findings

Higher accuracy in lesion prediction compared to baselines.

Reduced standard deviation of prediction errors.

Outperforms state-of-the-art CF-SAGAN model.

Abstract

Multiple Sclerosis (MS) is a chronic neurological condition characterized by the development of lesions in the white matter of the brain. T2-fluid attenuated inversion recovery (FLAIR) brain magnetic resonance imaging (MRI) provides superior visualization and characterization of MS lesions, relative to other MRI modalities. Follow-up brain FLAIR MRI in MS provides helpful information for clinicians towards monitoring disease progression. In this study, we propose a novel modification to generative adversarial networks (GANs) to predict future lesion-specific FLAIR MRI for MS at fixed time intervals. We use supervised guided attention and dilated convolutions in the discriminator, which supports making an informed prediction of whether the generated images are real or not based on attention to the lesion area, which in turn has potential to help improve the generator to predict the lesion area of future examinations more accurately. We compared our method to several baselines and one state-of-art CF-SAGAN model [1]. In conclusion, our results indicate that the proposed method achieves higher accuracy and reduces the standard deviation of the prediction errors in the lesion area compared with other models with similar overall performance.