Segmentation of Multiple Sclerosis lesions using 7T multi-contrast MRI data

Motivation: The effective treatment of Multiple sclerosis (MS) requires reliable estimates of lesion load and hence precise lesion detection over time. However, current lesion load estimation is either qualitative or too time-consuming. Goal(s): Our study automates MS lesion segmentation by training DeepMedic for application to 7T multi-contrast MRI data of MS patients. Approach: Training with all four contrasts achieved the best results compared to Lesion Segmentation Tool (LST)—a conventional/non-deep-learning SPM-based MS lesions segmentation approach. Results: Our study highlights potential for automating MS lesion detection/segmentation for 7T multi-contrast MRI data, underscoring the importance of accurate ground truth data and high-quality databases for improved detection accuracy. Impact: The results of this research will impact the user-independent detection/segmentation of multiple sclerosis lesions, making manual assessment by clinicians obsolete and enable fully automated monitoring of lesions load as a quantitative radiological marker of disease progression.