  In recent years, deep learning based methods have shown success in essential
medical image analysis tasks such as segmentation. Post-processing and refining
the results of segmentation is a common practice to decrease the
misclassifications originating from the segmentation network. In addition to
widely used methods like Conditional Random Fields (CRFs) which focus on the
structure of the segmented volume/area, a graph-based recent approach makes use
of certain and uncertain points in a graph and refines the segmentation
according to a small graph convolutional network (GCN). However, there are two
drawbacks of the approach: most of the edges in the graph are assigned randomly
and the GCN is trained independently from the segmentation network. To address
these issues, we define a new neighbor-selection mechanism according to feature
distances and combine the two networks in the training procedure. According to
the experimental results on pancreas segmentation from Computed Tomography (CT)
images, we demonstrate improvement in the quantitative measures. Also,
examining the dynamic neighbors created by our method, edges between
semantically similar image parts are observed. The proposed method also shows
qualitative enhancements in the segmentation maps, as demonstrated in the
visual results.
