Topology polymorphism graph for XXXX segmentation in PET-CT images.

@entity1 @entity5529 @entity5 @entity2 @entity1149
UNASSIGNED: Accurate @entity2 segmentation is problematic when the @entity5 boundary or edge, which reflects the advancing edge of the @entity5 , is difficult to discern on chest CT or PET. We propose a 'topo-poly' graph model to improve identification of the @entity5 extent. Our model incorporates an intensity graph and a topology graph. The intensity graph provides the joint PET-CT foreground similarity to differentiate the @entity5 from surrounding tissues. The topology graph is defined on the basis of contour tree to reflect the inclusion and exclusion relationship of regions. By taking into account different topology relations, the edges in our model exhibit topological polymorphism. These polymorphic edges in turn affect the energy cost when crossing different topology regions under a random walk framework, and hence contribute to appropriate @entity5 delineation. We validated our method on 40 @entity1 with @entity1149 where the @entity5 were manually delineated by a clinical expert. The studies were separated into an 'isolated' group (n = 20) where the @entity2 was located in the lung parenchyma and away from associated structures / tissues in the thorax and a 'complex' group (n = 20) where the @entity5 abutted / involved a variety of adjacent structures and had heterogeneous FDG uptake. The methods were validated using Dice's similarity coefficient (DSC) to measure the spatial volume overlap and @entity5529 ( @entity5529 ) to compare shape similarity calculated as the maximum surface distance between the segmentation results and the manual delineations. Our method achieved an average DSC of 0.881              0.046 and @entity5529 of 5.311              3.022   mm for the isolated cases and DSC of 0.870              0.038 and @entity5529 of 9.370              3.169   mm for the complex cases. Student's t-test showed that our model outperformed the other methods (p-values   <0.05).
