  More and more attention has been paid to the segmentation of pulmonary
nodules. Among the current methods based on deep learning, 3D segmentation
methods directly input 3D images, which takes up a lot of memory and brings
huge computation. However, most of the 2D segmentation methods with less
parameters and calculation have the problem of lacking spatial relations
between slices, resulting in poor segmentation performance. In order to solve
these problems, we propose an adjacent slice feature guided 2.5D network. In
this paper, we design an adjacent slice feature fusion model to introduce
information from adjacent slices. To further improve the model performance, we
construct a multi-scale fusion module to capture more context information, in
addition, we design an edge-constrained loss function to optimize the
segmentation results in the edge region. Fully experiments show that our method
performs better than other existing methods in pulmonary nodule segmentation
task.
