Joint segmentation and quantification of oncological lesions in PET/CT: Preliminary evaluation on a zeolite phantom

In this work we propose a strategy to jointly estimate activity and borders of oncological lesions in PET/CT. The starting step is constituted by a lesion contouring on PET image volume and by a Gaussian Mixture Model (GMM) clustering which individuates a set of regions in the lesion area (lesion, uncertainty, lesion spillout, organ). A maximum likelihood (A WOSEM) reconstruction step refines regions' borders and estimates a mean convergence activity for the lesion region. It applies a model of the scanner Point Spread Function (PSF) to recover blurring and it contemporaneously works on regional basis functions and single voxels. The area outside the four regions is frozen (i.e. not updated). The algorithm was validated on an anthropomorphic phantom in which lesions have been simulated with zeolites (clinoptilolite samples, volume 0.6 - 5.2 ml) loaded with
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F-FDG. Zeolite borders for ground truth definition were derived segmenting zeolites on coregistered CT images. For each zeolite, three different initial contouring were considered, corresponding to volumes about 100%, 60% and 140% of the true volume: the GMM clustering was able to robustly delineate regions independently from the initial contouring (variations <;8%). The reconstruction step succeeded in refining regions' borders (volume error <;17%; Dice index >0.75) and in estimating zeolite activity (activity error <; 11 % for zeolites >1ml). Suboptimal results were found for zeolites at the border of the axial FOV, since the PSF model, supposed invariant to axial shift, was inadequate at the axial borders. The proposed strategy appears promising and can be proposed as a general approach for a semi-automatic quantification and segmentation of lesions previously detected on standard clinical images. It will be further validated on data sets provided with a ground truth.