XXXX segmentation from PET-CT volumes with spatial-topological constraint.

@entity5 @entity1 @entity2 @entity2033
PURPOSE: Accurate @entity2 segmentation is a prerequisite for effective radiation therapy and surgical planning. However, @entity5 delineation is challenging when the @entity5 boundaries are indistinct on PET or CT. To address this problem, we developed a segmentation method to improve the delineation of primary @entity2 from PET-CT images. METHODS: We formulated the segmentation problem as a label information propagation process in an iterative manner. Our model incorporates spatial-topological information from PET and local intensity changes from CT. The topological information of the regions was extracted based on the metabolic activity of different tissues. The spatial-topological information moderates the amount of label information that a pixel receives: The label information attenuates as the spatial distance increases and when crossing different topological regions. Thus, the spatial-topological constraint assists accurate @entity5 delineation and separation. The label information propagation and transition model are solved under a random walk framework. RESULTS: Our method achieved an average DSC of [Formula: see text] and @entity2033 (mm) of [Formula: see text] on 40 @entity1 with @entity2 . The t test showed a significant improvement (p value  [Formula: see text]  0.05) in segmentation accuracy when compared to eight other methods. Our method was better able to delineate @entity5 that had heterogeneous FDG uptake and which abutted adjacent structures that had similar densities. CONCLUSIONS: Our method, using a spatial-topological constraint, provided @entity2 delineation, in particular, when the @entity5 involved or abutted the chest wall and the mediastinum.
