Abstract
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Objectives To develop an accurate segmentation method for primary lung tumors, in particular when the tumor has heterogeneous uptake on PET and boundary is difficult to discern on CT.
Methods In our MGM, the tumor-background likelihood (TBL) is calculated from CT and the topology information is extracted from PET. The model is developed in 3 stages: Stage 1: Extraction of information including (a) topology to reflect the inclusion or exclusion relation of regions. The topology was extracted by representing PET as a contour tree [1]. (b) TBL was estimated as the joint intensity similarity and spatial distance defined as the shortest Euclidean distance between a pixel and the tumor/background labels. The higher the distance cost, the lower the likelihood of the pixel and the seeds. Stage 2: MGM was constructed with an intensity graph to incorporate PET SUVs for tumor identification and TBL for anatomical boundary delineation. A topology graph, based on the contour tree, provided information for inhomogeneous region grouping; and then an inter-graph was derived to propagate the regional grouping information to pixel level and to provide an appropriate classification of the inhomogeneous FDG distribution within the tumor. Stage 3: Tumor segmentation with MGM used a Random Walk (RW) [2] framework. We validated our method on 40 NSCLC patient datasets with manual delineation by a clinical expert. The volumetric overlap was measured by Dice’s similarity coefficient (DSC).
Results Our method achieved a better average DSC of 0.842±0.050, when compared to 7 other approaches including SUV-2.5 (0.671 ± 0.120), 50% SUVmax (0.603 ± 0.098), an adaptive threshold based on mean SUV (0.574 ± 0.193), FCM (0.608 ± 0.209), TCD [4] (0.723 ± 0.086), RW from CT (0.787 ± 0.072) and TBLM [5] from PET-CT (0.813 ± 0.069).
Conclusions Our MGM improved segmentation accuracy for the identification of primary lung tumors where the tumors had indistinct margins and where there was inhomogeneous FDG uptake.