Joint variational segmentation of CT-PET data for tumoral lesions

Medical imaging, used for both diagnosis and therapy planning, is evolving towards multi-modality acquisition protocols. Manual segmentation of 3D images is a tedious task and prone to inter- and inter-experts variability. Moreover, the automatic segmentation exploiting the characteristics of multi-modal images is still a difficult problem. In this paper, we propose the use of a variational segmentation method, based on the minimization of the TV norm and a convex formulation, for segmenting thoracic pairs of PET and CT images, in the context of radiotherapy planning. We first highlight the limitations of a pure vectorial formulation of the variational segmentation method for PET and CT images. We then propose to better exploit the bi-modality by introducing a parameter which varies spatially depending on the PET intensity to adjust precisely the segmentation of CT images. Segmentation results on lung tumors and lymphatic nodes are shown, and comparisons performed with manual segmentations illustrate the quality of the results.