IE-Vnet: Deep Learning-Based Segmentation of the Inner Ear's Total Fluid Space

Background: In-vivo MR-based high-resolution volumetric quantification methods of the endolymphatic hydrops (ELH) are highly dependent on a reliable segmentation of the inner ear's total fluid space (TFS). This study aimed to develop a novel open-source inner ear TFS segmentation approach using a dedicated deep learning (DL) model. Methods: The model was based on a V-Net architecture (IE-Vnet) and a multivariate (MR scans: T1, T2, FLAIR, SPACE) training dataset (D1, 179 consecutive patients with peripheral vestibulocochlear syndromes). Ground-truth TFS masks were generated in a semi-manual, atlas-assisted approach. IE-Vnet model segmentation performance, generalizability, and robustness to domain shift were evaluated on four heterogenous test datasets (D2-D5, n = 4 x 20 ears). Results: The IE-Vnet model predicted TFS masks with consistently high congruence to the ground-truth in all test datasets (Dice overlap coefficient: 0.9 +/- 0.02, Hausdorff maximum surface distance: 0.93 +/- 0.71 mm, mean surface distance: 0.022 +/- 0.005 mm) without significant difference concerning side (two-sided Wilcoxon signed-rank test, p > 0.05), or dataset (Kruskal-Wallis test, p > 0.05; post-hoc Mann-Whitney U, FDR-corrected, all p > 0.2). Prediction took 0.2 s, and was 2,000 times faster than a state-of-the-art atlas-based segmentation method. Conclusion: IE-Vnet TFS segmentation demonstrated high accuracy, robustness toward domain shift, and rapid prediction times. Its output works seamlessly with a previously published open-source pipeline for automatic ELS segmentation. IE-Vnet could serve as a core tool for high-volume trans-institutional studies of the inner ear. Code and pre-trained models are available free and open-source under https://github.com/pydsgz/IEVNet.

Automated summary

This study developed a novel open-source segmentation approach for inner ear total fluid space (TFS) using deep learning. The model demonstrated high accuracy, robustness, and fast prediction times. It can be seamlessly integrated with existing tools for automatic endolymphatic hydrops (ELH) segmentation.