Enhancing precision in multiple sclerosis lesion segmentation: A U-net based machine learning approach with data augmentation

Abstract

Segmentation of Multiple Sclerosis (MS) lesions from Magnetic Resonance Imaging (MRI) data presents a significant challenge due to the necessity for large volumes of training data and a sophisticated training process. Traditional MRI datasets often lack the extensive sample sizes required for effective training, necessitating the exploration of alternative methods for accurate segmentation. This study proposes a robust machine learning algorithm designed to identify MS lesions using both single-modal and multi-modal MRI data. The proposed algorithm employs Convolutional Neural Networks (CNNs) in the form of U-Net architecture, a renowned model for biomedical image segmentation. To address the issue of insufficient training data, data augmentation techniques have been implemented, enhancing the diversity and volume of the training set. The dataset for this study was created from MRI data of 20 subjects. The algorithm's effectiveness was evaluated using the DSC score, a statistical tool that measures the similarity between two samples. The model achieved a DSC score of 0.7960 in the training set and 0.7912 in the test set, demonstrating its effectiveness in performing segmentation of MS from multi-modal MRI data. The predicted locations of MS lesions were compared with the corresponding layers of white matter, gray matter, and cerebrospinal fluid within the brain. This innovative approach aims to enhance the accuracy and efficiency of MS lesion segmentation, contributing to advancements in precision medicine and the overall understanding of MS.