Automated Radiomic Analysis of Vestibular Schwannomas and Inner Ears Using Contrast-Enhanced T1-Weighted and T2-Weighted Magnetic Resonance Imaging Sequences and Artificial Intelligence

Abstract

Objective To objectively evaluate vestibular schwannomas (VSs) and their spatial relationships with the ipsilateral inner ear (IE) in magnetic resonance imaging (MRI) using deep learning. Study Design Cross-sectional study. Patients A total of 490 adults with VS, high-resolution MRI scans, and no previous neurotologic surgery. Interventions MRI studies of VS patients were split into training (390 patients) and test (100 patients) sets. A three-dimensional convolutional neural network model was trained to segment VS and IE structures using contrast-enhanced T1-weighted and T2-weighted sequences, respectively. Manual segmentations were used as ground truths. Model performance was evaluated on the test set and on an external set of 100 VS patients from a public data set (Vestibular-Schwannoma-SEG). Main Outcome Measure(s) Dice score, relative volume error, average symmetric surface distance, 95th-percentile Hausdorff distance, and centroid locations. Results Dice scores for VS and IE volume segmentations were 0.91 and 0.90, respectively. On the public data set, the model segmented VS tumors with a Dice score of 0.89 ± 0.06 (mean ± standard deviation), relative volume error of 9.8 ± 9.6%, average symmetric surface distance of 0.31 ± 0.22 mm, and 95th-percentile Hausdorff distance of 1.26 ± 0.76 mm. Predicted VS segmentations overlapped with ground truth segmentations in all test subjects. Mean errors of predicted VS volume, VS centroid location, and IE centroid location were 0.05 cm3, 0.52 mm, and 0.85 mm, respectively. Conclusions A deep learning system can segment VS and IE structures in high-resolution MRI scans with excellent accuracy. This technology offers promise to improve the clinical workflow for assessing VS radiomics and enhance the management of VS patients.

Deep Learning Method for Rapid Simultaneous Multistructure Temporal Bone Segmentation

“…Images with metallic artifacts or motion artifacts that hindered clear visualization of anatomical structures were excluded. A trained otolaryngologist (CAN) with 15 years of practice and 4 years of experience in radiologic anatomical segmentation [21][22][23][24] performed the manual annotation. The anatomical structures of interest were manually annotated in all scans of the data set (n = 325), and the manually segmented structures were considered as the ground truth.…”