Deep Learning Consensus-based Annotation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Annotated Multi-Center Routine Clinical Dataset (Vestibular-Schwannoma-MC-RC 2)

The “Deep Learning Consensus-based Annotation of Vestibular Schwannoma from Magnetic Resonance Imaging: An Annotated Multi-Center Routine Clinical Dataset” (Vestibular-Schwannoma-MC-RC-2) comprises 190 adult patients with unilateral vestibular schwannoma (VS), referred to King’s College Hospital, London, UK. Patients with neurofibromatosis type 2 (NF2) were excluded. Each patient has 1–8 longitudinal scans acquired from 2010 onwards, totaling 543 contrast-enhanced T1-weighted (T1CE) scans and 133 T2 scans across 621 time points. The dataset provides binary VS segmentations for 534 T1CE scans, along with demographic data (sex, ethnicity, age) and clinical decisions recorded at each time point. Segmentations were created using an iterative, consensus-based deep learning approach. This resource supports research on automated VS surveillance, tumour segmentation, longitudinal growth modeling, and clinical decision support.