Joint Vestibular Schwannoma Enlargement Prediction and Segmentation Using a Deep Multi‐task Model

To develop a deep‐learning‐based multi‐task (DMT) model for joint tumor enlargement prediction (TEP) and automatic tumor segmentation (TS) for vestibular schwannoma (VS) patients using their initial diagnostic contrast‐enhanced T1‐weighted (ceT1) magnetic resonance images (MRIs). The segmentation result is significantly better than the separate TS network (dice coefficient of 83.13%, p = 0.03) and marginally lower than the state‐of‐the‐art segmentation model nnU‐Net (dice coefficient of 86.45%, p = 0.16). The proposed DMT model has higher learning efficiency and achieves promising performance on TEP and TS. The proposed technology has the potential to improve VS patient management.