Enhanced Detection of White Matter Hyperintensities via Deep Learning-Enabled MR Imaging Segmentation

Abstract

The segmentation of white matter abnormalities is crucial for the early diagnosis of cerebral diseases, which aids in minimizing the resultant physical and cognitive deficits. Automated segmentation methods are instrumental for the precise and early identification of white matter hyperintensities (WMH) from magnetic resonance (MR) images. In this investigation, datasets comprising ischemic stroke and WMH cases, imaged with the FLAIR (fluid-attenuated inversion recovery) MR sequence, were utilized due to their enhanced visibility of hyperintensities. For segmentation, the Mask R-CNN model, a sophisticated deep learning architecture, was finely adjusted to bolster its performance. Concurrently, the U-Net model, renowned for its efficacy in medical image segmentation, was employed. A comprehensive comparison of the two models' performance was conducted. Results demonstrate that the Mask R-CNN model achieved dice similarity coefficient (DSC) scores of 0.93 for the stroke dataset and 0.83 for the WMH dataset. The U-Net model yielded DSC scores of 0.92 and 0.82 for the respective datasets. These findings indicate an improvement over preceding studies in WMH segmentation accuracy utilizing the Mask R-CNN approach. It is concluded that automated WMH segmentation on MR images serves as a robust decision-support tool for clinicians during preliminary evaluations, although it should be noted that definitive disease detection necessitates the corroboration of clinical findings.