• Aucun résultat trouvé

Detection of bladder metabolic artifacts in (18)F-FDG PET imaging.

N/A
N/A
Protected

Academic year: 2021

Partager "Detection of bladder metabolic artifacts in (18)F-FDG PET imaging."

Copied!
13
0
0

Texte intégral

Loading

Figure

Figure 1: Three examples of PET acquisition of locally-advanced cervi- cervi-cal cancer, demonstrating a continuum uptake between tumor and  blad-der hyperfixation
Figure 2: Overall framework of the proposed methodology for semi-automatically detecting the bladder metabolic artifacts
Figure 3: Example of pelvic region cropped according to coxal bone structures on CT images
Figure 4: SUV-based k-means clustering example. (a) Original PET exam. (b) k-means clustering result: (green) corresponds to Cluster {A, T }, (red) to Cluster {I}, and (blue) to Cluster {B}
+6

Références

Documents relatifs

Two observers performed manual delineation of the tumors on the CT images and delineated the tumor uptake on the corresponding PET images manually, using a fixed threshold

La mandat de la Commission d’étude nous entraîne collectivement dans le débat sur l’éducation permanente et nous serons confrontés aux mêmes limites: celle d’une conception

Indeed, the antigens identified within these islands correspond to proteins from several different protein categories, mostly assigned to the cell wall and cellular processes and

One of our patients presented with posterior scleritis only and developed anterior scleritis years later in the same eye, and three other eyes changed the type of scleritis (Table 5

Delémont Develier Courroux Courcelon Vicques Courrendlin Châtillon Courfaivre Bassecourt Rossemaison Courtételle Bienne 40 min Bienne 40 min Bâle 40 min Bâle 40 min Belfort 50

Dans certains cas, on vous demandera également d’arrêter la metformine 48 heures avant l’examen (en cas d’injection de produit de contraste iodé durant l’examen). Que

To segment tumor in PET images using multiple features, the spatial evidential clustering algorithm, e.g., SECM [8], is fully improved by integrating unsupervised distance

The proposed framework con- sists of 3 steps: seed detection giving a set of seeds susceptible to belong to the tumor; seed selection allowing to delete aberrant seeds and to