ABSTRACT
Background and Purpose
Automated detection of focal cortical dysplasia (FCD) requires large volumes of voxelwise-lesion-delineated MRI data, which are difficult to acquire. This study aims to generate synthetic MRI data exhibiting FCD, assess its realism, and evaluate its impact on automated FCD detection—particularly in reducing the need for manual annotations.
Methods
T1-weighted (T1w) and T2-weighted-fluid-attenuated inversion recovery (FLAIR) MRI scans from 131 FCD patients and 90 healthy controls from multiple (3) sites were retrospectively studied. Synthetic MRIs were generated by conditioning a generative network on binary FCD mask. Two neuroradiologists identified real images from a random set of 14 real and 14 synthetic scans. Three nnU-Net models were trained to detect FCD using (i) real-only (35-FCD/35-controls), (ii) real (35-FCD/35-controls) + synthetic augmentation, and (iii) expanded real data (70-FCD/70 controls).
Results
Experts showed limited ability to distinguish real from synthetic images, with classification accuracy of 60% for T1w and 70% for FLAIR (inter-rater agreement κ = 0.86). Augmenting automated FCD detection with synthetic data increased sensitivity by 8.14% (p = 0.12) and improved model confidence at true lesion sites (0.83 ± 0.11 to 0.89 ± 0.12; p = 0.02). The expanded real-data model further improved sensitivity to 73.8% (p < 0.001) and confidence to 0.90 ± 0.14 (p = 0.01).
Conclusion
Conditional generative networks can generate realistic synthetic FCD-MRIs, reducing labeled data needs by ∼20% while maintaining equivalent sensitivity. Equivalent amounts of real data, when available, remain more effective than synthetic augmentation.
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This post is Copyright: Prabhjot Kaur,
Hakim Ouaalam,
Sedat Kandemirli,
Sanjay P. Prabhu,
Simon K. Warfield | June 4, 2026
Wiley: Journal of Neuroimaging: Table of Contents