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

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.

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.

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.

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.

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