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More and more attention has been paid to the segmentation of pulmonarynodules. Among the current methods based on deep learning, 3D segmentationmethods directly input 3D images, which takes up a lot of memory and bringshuge computation. However, most of the 2D segmentation methods with lessparameters and calculation have the problem of lacking spatial relationsbetween slices, resulting in poor segmentation performance. In order to solvethese problems, we propose an adjacent slice feature guided 2.5D network. Inthis paper, we design an adjacent slice feature fusion model to introduceinformation from adjacent slices. To further improve the model performance, weconstruct a multi-scale fusion module to capture more context information, inaddition, we design an edge-constrained loss function to optimize thesegmentation results in the edge region. Fully experiments show that our methodperforms better than other existing methods in pulmonary nodule segmentationtask.

Adjacent Slice Feature Guided 2.5D Network for Pulmonary Nodule Segmentation