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

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.