Synchrotron‐Based Deep Learning Network of the Inner Ear: Development and Expert Validation

OBJECTIVE: To develop and externally validate a deep learning segmentation network capable of automatically segmenting the inner ear in preoperative clinical computed tomography (CT) scans across various resolutions and protocols. METHODS: A deep learning-based segmentation network was developed using 100 cadaveric specimens that were scanned with synchrotron-radiation phase contrast imaging (SR-PCI) and various clinical CT scanners. Different acquisitions, protocols, and augmentations were used to create a total of 4,784 paired SR-PCI and clinical three-dimensional datasets used for deep learning training and model development. Performance and accuracy of the network were assessed on a separate unseen dataset and externally validated against manual segmentations from seven individual domain experts (otologists and radiologists), the mean expert performance, and a simultaneous truth and performance level (STAPLE) consensus segmentation. RESULTS: The network pipeline significantly outperformed each individual expert segmentation, the average of the expert segmentations, and the STAPLE consensus segmentation. Compared to the SR-PCI ground truth data, the network achieved a Dice similarity coefficient of 0.922, a maximum absolute Hausdorff distance of 0.329 mm, and an average Hausdorff distance of 0.006 mm on cone-beam CT and helical CT with resolutions as low as 625 μm. CONCLUSION: This is the first automated segmentation algorithm for the inner ear that has been shown to outperform segmentations from domain experts, establishing a new clinical gold standard. LEVEL OF EVIDENCE: N/A.