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. 2025 Nov 19.
doi: 10.1111/epi.70007. Online ahead of print.

Convolutional neural networks for automatic tuber segmentation and quantification of tuber burden in tuberous sclerosis complex

Iván Sánchez Fernández  1 , Matheus D Soldatelli  2 , Gillian N Miller  3 , Catharina F Gout  1   4   5 , Ellen C Broekhuizen  1   6 , Iris C J den Hertog  1   7 , Daan A Pijs  1 , Errol Apostolopoulos  1 , Prabhjot Kaur  2 , Abdelhakim Ouaalam  2 , Martina E Bebin  8 , Hope Northrup  9 , Darcy A Krueger  10 , Joyce Y Wu  11 , Alexander L Cohen  3   12 , Mustafa Sahin  12   13 , Davood Karimi  2 , Simon K Warfield  2 , Jurriaan M Peters  1   14 ; TACERN Study Group and the Developmental Synaptopathies Consortium–RDCRN Study Group
Collaborators, Affiliations

Convolutional neural networks for automatic tuber segmentation and quantification of tuber burden in tuberous sclerosis complex

Iván Sánchez Fernández et al. Epilepsia. .

Abstract

Objective: This study was undertaken to develop a fully automated algorithm for tuber segmentation and quantification of tuber volume that performs similarly to the gold standard human neuroradiologist.

Methods: We used brain magnetic resonance imaging (MRI) from patients with tuberous sclerosis complex (TSC) to train and validate a convolutional neural network (CNN), which was evaluated on segmentation with the Dice-Sørensen similarity coefficient (DSSC) and on tuber burden quantification with Spearman correlation coefficient against a neuroradiologist's gold standard in the test set.

Results: We collected 263 MRIs from 196 patients (57% males) with median (25th percentile-75th percentile) age of 4.3 (3.0-10.1) years: 176 MRIs in the train set, 39 in the validation set, and 48 in the test set. The final model achieved in the test set a DSSC of .820 (95% confidence interval [CI] = .799-.840) in the whole brain and in the different lobes the following: .831 (95% CI = .804-.850) in left frontal, .827 (95% CI = .799-.853) in right frontal, .817 (95% CI = .779-.842) in left temporal, .834 (95% CI = .812-.849) in right temporal, .821 (95% CI = .783-.856) in left parietal, .840 (95% CI = .810-.865) in right parietal, .832 (95% CI = .808-.851) in left occipital, and .856 (95% CI = .838-.871) in right occipital. CNN tuber volume quantification nearly perfectly correlated (Spearman correlation coefficient) with the neuroradiologist's across the whole brain (.984, 95% CI = .971-.991) and in the different lobes: .966 (95% CI = .940-.981) in left frontal, .973 (95% CI = .952-.985) in right frontal, .936 (95% CI = .888-.964) in left temporal, .967 (95% CI = .942-.982) in right temporal, .989 (95% CI = .980-.994) in left parietal, .983 (95% CI = .970-.990) in right parietal, .992 (95% CI = .985-.995) in left occipital, and .982 (95% CI = .968-.990) in right occipital (all p < .00001).

Significance: We generated, trained, validated, and made publicly available a CNN that achieves a near-perfect correlation with a neuroradiologist gold standard quantification of tuber burden, allows for objective tuber segmentation, and increases rigor and reproducibility in TSC research across institutions.

Keywords: convolutional neural network; deep learning; neuroradiology; tuber burden; tuberous sclerosis complex.

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References

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