  Neurofibromatosis type 1 (NF1) is an autosomal dominant tumor predisposition
syndrome that involves the central and peripheral nervous systems. Accurate
detection and segmentation of neurofibromas are essential for assessing tumor
burden and longitudinal tumor size changes. Automatic convolutional neural
networks (CNNs) are sensitive and vulnerable as tumors' variable anatomical
location and heterogeneous appearance on MRI. In this study, we propose deep
interactive networks (DINs) to address the above limitations. User interactions
guide the model to recognize complicated tumors and quickly adapt to
heterogeneous tumors. We introduce a simple but effective Exponential Distance
Transform (ExpDT) that converts user interactions into guide maps regarded as
the spatial and appearance prior. Comparing with popular Euclidean and geodesic
distances, ExpDT is more robust to various image sizes, which reserves the
distribution of interactive inputs. Furthermore, to enhance the tumor-related
features, we design a deep interactive module to propagate the guides into
deeper layers. We train and evaluate DINs on three MRI data sets from NF1
patients. The experiment results yield significant improvements of 44% and 14%
in DSC comparing with automated and other interactive methods, respectively. We
also experimentally demonstrate the efficiency of DINs in reducing user burden
when comparing with conventional interactive methods. The source code of our
method is available at \url{https://github.com/Jarvis73/DINs}.
