Co-Registered Volumetric MRI-Based Synthetic Lesion Transplantation for Multiple Sclerosis Classification Using Cutoff Augmentation | ||
| International Journal of Intelligent Computing and Information Sciences | ||
| Volume 25, Issue 3, September 2025, Pages 41-54 PDF (1.36 M) | ||
| Document Type: Original Article | ||
| DOI: 10.21608/ijicis.2025.402810.1410 | ||
| Authors | ||
| Rezq Muhammed Thabet* 1; Dina Khattab2; Maryam ElBery2; howida shedeed2 | ||
| 1Department of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt | ||
| 2Department of Scientific Computing, Faculty of Computer and Information Sciences, Ain Shams University, Cairo, Egypt | ||
| Abstract | ||
| Multiple sclerosis (MS) is a chronic, immune-mediated disorder characterized by demyelinating lesions visible on MRI. Limited volumetric datasets and class imbalance hinder automated deep-learning approaches for MS classification. In this study, we extend the cutoff augmentation technique to three-dimensional (3D) MRI volumes and evaluate it using two publicly available pathological MS datasets and a large healthy cohort. Specifically, we used the 3D-MR-MS dataset (30 MS patients; modalities: FLAIR, T1-w, contrast-enhanced T1-w, and T2-w with multi-rater lesion segmentations), the long-MR-MS dataset (20 patients imaged longitudinally, two sessions per patient, with lesion-change masks), and the IXI healthy cohort (≈600 subjects; we selected T1-w volumes and used 50 T1-w scans for fold-specific pairing and background templates). After co-registering anatomical images, lesion masks, and brain masks into a shared healthy reference space, and performing skull stripping and intensity normalization, lesion voxels were transplanted into healthy volumes to generate synthetic pathological examples. A 3D DenseNet-169 trained with five-fold cross-validation demonstrated that 3D cutoff augmentation increased the mean accuracy from 61.2% to 72.7%, doubled the MS recall from 22.3% to 45.4%, and improved the F1 score from 36.3% to 61.7% while preserving precision. These results indicate that co-registered 3D cutoff augmentation effectively mitigates data scarcity and class imbalance for volumetric MS classification. | ||
| Keywords | ||
| Multiple Sclerosis; MRI Modalities; Cutoff Augmentation; CAD tools; Co-Registration | ||
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