Service interruption on Monday 11 July from 12:30 to 13:00: all the sites of the CCSD (HAL, EpiSciences, SciencesConf, AureHAL) will be inaccessible (network hardware connection).
Skip to Main content Skip to Navigation
Journal articles

Hybrid PET/MRI co-segmentation based on joint fuzzy connectedness and graph cut

Abstract : BACKGROUND AND OBJECTIVE: Tumor segmentation from hybrid PET/MRI scans may be highly beneficial in radiotherapy treatment planning. Indeed, it gives for both modalities the suitable information that could make the delineation of tumors more accurate than using each one apart. We aim in this work to propose a co-segmentation method that deals with several challenges, notably the lack of one-to-one correspondence between tumors of the two modalities and the boundaries' smoothing. METHODS: The proposed method is designed to surpass these limits, we propose a segmentation method based on the GCsummax technique. The method takes the advantage of Iterative Relative Fuzzy Connectedness (IRFC) on seeds initialization, and the standard min-cut/max-flow technique for the boundary smoothing. Seed initialization was accurately performed thanks to high uptake regions on PET. Besides, a visibility weighting scheme was adapted to achieve the task of co-segmentation using the IRFC algorithm. Then, given the co-segmented regions, we introduce a morphological-based technique that provides object seeds to standard Graph Cut (GC) allowing it to avoid the shrinking problem. Finally, for each modality, the segmentation task is formulated as an energy minimization problem which is resolved by a min-cut/max-flow technique. RESULTS: The overlap ratio (denoted DSC) between our segmentation results and the ground-truth for PET images is 92.63  ±  1.03, while the DSC for MRI images is 90.61  ±  3.70. CONCLUSIONS: The proposed method was tested on different types of diseases and it outperformed the state-of-the-art methods. We show its superiority in terms of assymetric relation between PET and MRI and tumors heterogeneity.
Document type :
Journal articles
Complete list of metadata
Contributor : AbdelHakim Ben Hassine Connect in order to contact the contributor
Submitted on : Tuesday, September 26, 2017 - 11:15:20 AM
Last modification on : Saturday, May 21, 2022 - 3:42:40 AM



Arafet Sbei, Khaoula Elbedoui, Walid Barhoumi, Philippe Maksud, Chokri Maktouf. Hybrid PET/MRI co-segmentation based on joint fuzzy connectedness and graph cut. Computer Methods and Programs in Biomedicine, Elsevier, 2017, 149, pp.29 - 41. ⟨10.1016/j.cmpb.2017.07.006⟩. ⟨pasteur-01593403⟩



Record views