Deep Learning for Automatic Segmentation of Vestibular Schwannoma: A Retrospective Study from Multi-Centre Routine MRI Provisionally Accepted

Aaron Kujawa^1* Reuben Dorent¹ Steve Connor^{1, 2, 3} Suki Thomson² Marina Ivory¹ Ali Vahedi² Emily Guilhem² Navodini Wijethilake¹ Robert Bradford^{4, 5} Neil Kitchen^{4, 5} Sotirios Bisdas⁵ Sebastien Ourselin¹ Tom Vercauteren¹ Jonathan Shapey^{1, 6}

• ¹King's College London, United Kingdom
• ²Department of Neuroradiology, King's College Hospital, United Kingdom
• ³Service of Radiology, Guy's and St Thomas' NHS Foundation Trust, United Kingdom
• ⁴Queen Square Radiosurgery Centre (Gamma Knife), National Hospital for Neurology and Neurosurgery, United Kingdom
• ⁵Department of Neurosurgery, National Hospital for Neurology and Neurosurgery, United Kingdom
• ⁶Department of Neurosurgery, King's College Hospital NHS Foundation Trust, United Kingdom

Automatic segmentation of vestibular schwannoma (VS) from routine clinical MRI has potential to improve clinical workflow, facilitate treatment decisions, and assist patient management. Previous work demonstrated reliable automatic segmentation performance on datasets of standardised MRI images acquired for stereotactic surgery planning. However, diagnostic clinical datasets are generally more diverse and pose a larger challenge to automatic segmentation algorithms, especially when post-operative images are included. In this work, we show for the first time that automatic segmentation of VS on routine MRI datasets is also possible with high accuracy.We acquired and publicly release a curated multi-centre routine clinical (MC-RC) dataset of 160 patients with a single sporadic VS. For each patient up to three longitudinal MRI exams with contrast-enhanced T1-weighted (ceT1w) (n=124) and T2-weighted (T2w) (n=363) images were included and the VS manually annotated. Segmentations were produced and verified in an iterative process: 1) initial segmentations by a specialized company; 2) review by one of three trained radiologists; and 3) validation by an expert team. Inter-and intra-observer reliability experiments were performed on a subset of the dataset. A state-of-the-art deep learning framework was used to train segmentation models for VS. Model performance was evaluated on a MC-RC hold-out testing set, another public VS datasets, and a partially public dataset.1 Kujawa et al.The generalizability and robustness of the VS deep learning segmentation models increased significantly when trained on the MC-RC dataset. Dice similarity coefficients (DSC) achieved by our model are comparable to those achieved by trained radiologists in the inter-observer experiment. On the MC-RC testing set, median DSCs were 86.2(9.5) for ceT1w, 89.4(7.0) for T2w and 86.4(8.6) for combined ceT1w+T2w input images. On another public dataset acquired for Gamma Knife stereotactic radiosurgery our model achieved median DSCs of 95.3(2.9), 92.8(3.8), and 95.5(3.3), respectively. In contrast, models trained on the Gamma Knife dataset did not generalise well as illustrated by significant underperformance on the MC-RC routine MRI dataset, highlighting the importance of data variability in the development of robust VS segmentation models.The MC-RC dataset and all trained deep learning models were made available online.