Active Surface for Fully 3D Automatic Segmentation

For tumor delineation in Positron Emission Tomography (PET) images, it is of utmost importance to devise efficient and operator-independent segmentation methods capable of reconstructing the 3D tumor shape.In this paper, we present a fully 3D automatic system for the brain tumor delineation in PET images. In previous work, we proposed a 2D segmentation system based on a two-steps approach. The first step automatically identified the slice enclosing the maximum tracer uptake in the whole tumor volume and generated a rough contour surrounding the tumor itself. Such contour was then used to initialize the second step, where the 3D shape of the tumor was obtained by separately segmenting 2D slices. In this paper, we migrate our system into fully 3D. In particular, the segmentation in the second step is performed by evolving an active surface directly in the 3D space. The key points of such advancement are that it performs the shape reconstruction on the whole stack of slices simultaneously, leveraging useful cross-slice information. Additionally, it does not require any specific stopping condition, as the active surface naturally reaches a stable topology once convergence is achieved.Performance of this approach is evaluated on the same dataset discussed in our previous work to assess if any benefit is achieved migrating the system from 2D to 3D. Results confirm an improvement in performance in term of dice similarity coefficient (89.89%), and Hausdorff distance (1.11 voxel).