Abstract
Focal cortical dysplasia (FCD) is among the leading causes of drug-resistant focal epilepsy and can be challenging to detect on routine MRI due to the subtle and heterogeneous appearance of lesions. Motivatedbythescarcityofclinicallyrepresentativecohorts,weinvestigate the feasibility of simulating FCD-like lesions in healthy MRI using a composition of hand-crafted image transformations. We develop SynthFCD, a phenomenological FCD simulator, and create 2,164 synthetic examplesfromhealthycontrolsintheheterogeneousFOMO300k dataset. We then pretrain deep learning (DL) segmentation models on synthetic lesions and fine-tune them on real FCD cases from the University Hospital Bonn (UHB) cohort. Comprehensive evaluations show consistent improvements across simulator presets, with detection-rate gains of up to 18.5 percentage points over training from scratch and 7.4 points over self-supervisedpretraining,whileachievingstate-of-the-artsegmentation performance (0.366 DSC) on the challenging held-out UHB benchmark. These findings suggest that simulation-based synthetic data generation canbridgeexistingradiologicalknowledgeandadvancesinDLtoprovide meaningfulsupervisorysignalsthatcouldultimatelyaidthedetectionof underrepresented and diagnostically challenging cases. ⋆ Corresponding author. 2 P. Koutsouvelis et al.
Links to Paper and Supplementary Materials
Main Paper (Open Access Version): https://papers.miccai.org/miccai-2026-sat/paper/SASHIMI_065.pdf
SharedIt Link: Not yet available
SpringerLink (DOI): Not yet available
Supplementary Material: Not Submitted
Link to Open Review
Open Review Page: https://openreview.net/forum?id=rDcp845sDT
BibTex
@InProceedings{KouPet_Simulating_MICCAISAT2026,
author = { Koutsouvelis, Petros AND Amirrajab, Sina AND Volmer, Leroy AND Eekers, Daniëlle B. P. AND Schijns, Olaf E. M. G. AND Brecheisen, Ralph AND Dekker, Andre},
title = { { Simulating the Radiological Appearance of Focal Cortical Dysplasia in MRI } },
booktitle = {Medical Image Computing and Computer Assisted Intervention -- MICCAI 2026 Workshops and Challenges},
year = {2026},
publisher = {Springer Nature Switzerland},
volume = {LNCS 17258},
month = {pending},
page = {pending}
}