Vestibular schwannoma growth prediction from longitudinal MRI by time-conditioned neural fields

Vestibular schwannomas (VS) are benign tumors that are generally managed by active surveillance with MRI examination. To further assist clinical decision-making and avoid overtreatment, an accurate prediction of tumor growth based on longitudinal imaging is highly desirable. In this paper, we introduce DeepGrowth, a deep learning method that incorporates neural fields and recurrent neural networks for prospective tumor growth prediction. In the proposed method, each tumor is represented as a signed distance function (SDF) conditioned on a low-dimensional latent code. Unlike previous studies, we predict the latent codes of the future tumor and generate the tumor shapes from it using a multilayer perceptron (MLP). To deal with irregular time intervals, we introduce a time-conditioned recurrent module based on a ConvLSTM and a novel temporal encoding strategy, which enables the proposed model to output varying tumor shapes over time. The experiments on an in-house longitudinal VS dataset showed that the proposed model significantly improved the performance (>=1.6% Dice score and >=0.20 mm 95% Hausdorff distance), in particular for top 20% tumors that grow or shrink the most (>=4.6% Dice score and >= 0.73 mm 95% Hausdorff distance). Our code is available at https://github.com/cyjdswx/DeepGrowth.