Convolutional neural networks for automatic tuber segmentation and quantification of tuber burden in tuberous sclerosis complex.
Authors
Affiliations (14)
Affiliations (14)
- Localization Laboratory, Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- Department of Radiology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- Laboratory of Translational Neuroimaging, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- Department of Pediatrics, Noordwest Ziekenhuisgroep, Alkmaar, the Netherlands.
- Vrije Universiteit, Amsterdam, the Netherlands.
- Utrecht University, Utrecht, the Netherlands.
- University of Twente, Enschede, the Netherlands.
- Department of Neurology, University of Alabama at Birmingham, Birmingham, Alabama, USA.
- Department of Pediatrics, McGovern Medical School at University of Texas Health Science Center at Houston and Children's Memorial Hermann Hospital, Houston, Texas, USA.
- Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
- Ann and Robert H. Lurie Children's Hospital of Chicago, Northwestern University Feinberg School of Medicine, Chicago, Illinois, USA.
- Department of Neurology, Boston Children's Hospital, Boston, Massachusetts, USA.
- Rosamund Stone Zander Translational Neuroscience Center, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
- Division of Epilepsy and Clinical Neurophysiology, Department of Neurology, Boston Children's Hospital, Harvard Medical School, Boston, Massachusetts, USA.
Abstract
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. 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. 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). 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.