"Fully Automated 3D Vestibular Schwannoma Segmentation with and without Gadolinium Contrast: a multi-center, multi-vendor study"

Olaf Neve, Yunjie Chen, Qian Tao, Stephan Romeijn, Nick de Boer, Willem Grootjans, Mark Kruit, Boudewijn Lelieveldt, Jeroen Jansen, Erik Hensen, Berit Verbist and Marius Staring

Abstract

Purpose: To develop automated vestibular schwannoma measurements on contrast-enhanced T1- and T2-weighted MRI.

Material and methods: MRI data from 214 patients in 37 different centers was retrospectively analyzed between 2020-2021. Patients with hearing loss (134 vestibular schwannoma positive [mean age ± SD, 54 ± 12 years; 64 men], 80 negative) were randomized to a training and validation set and an independent test set. A convolutional neural network (CNN) was trained using five-fold cross-validation for two models (T1 and T2). Quantitative analysis including Dice index, Hausdorff distance, surface-to-surface distance (S2S), and relative volume error were used to compare the computer and the human delineations. Furthermore, an observer study was performed in which two experienced physicians evaluated both delineations.

Results: The T1-weighted model showed state-of-the-art performance with a mean S2S distance of less than 0.6 mm for the whole tumor and the intrameatal and extrameatal tumor parts. The whole tumor Dice index and Hausdorff distance were 0.92 and 2.1 mm in the independent test set. T2-weighted images had a mean S2S distance less than 0.6 mm for the whole tumor and the intrameatal and extrameatal tumor parts. Whole tumor Dice index and Hausdorff distance were 0.87 and 1.5 mm in the independent test set. The observer study indicated that the tool was comparable to human delineations in 85-92% of cases.

Conclusion: The CNN model detected and delineated vestibular schwannomas accurately on contrast-enhanced T1 and T2-weighted MRI and distinguished the clinically relevant difference between intrameatal and extrameatal tumor parts.

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