title: Accurate automatic delineation of heterogeneous functional volumes in positron emission tomography for oncology applications.
abstract: OBJECTIVE
  Accurate contouring of positron emission tomography (PET) functional volumes is now considered crucial in image-guided radiotherapy and other oncology applications because the use of functional imaging allows for biological target definition. In addition, the definition of variable uptake regions within the tumor itself may facilitate dose painting for dosimetry optimization.
  
  
  METHODS
  Current state-of-the-art algorithms for functional volume segmentation use adaptive thresholding. We developed an approach called fuzzy locally adaptive Bayesian (FLAB), validated on homogeneous objects, and then improved it by allowing the use of up to three tumor classes for the delineation of inhomogeneous tumors (3-FLAB). Simulated and real tumors with histology data containing homogeneous and heterogeneous activity distributions were used to assess the algorithm's accuracy.
  
  
  RESULTS
  The new 3-FLAB algorithm is able to extract the overall tumor from the background tissues and delineate variable uptake regions within the tumors, with higher accuracy and robustness compared with adaptive threshold (T(bckg)) and fuzzy C-means (FCM). 3-FLAB performed with a mean classification error of less than 9% +/- 8% on the simulated tumors, whereas binary-only implementation led to errors of 15% +/- 11%. T(bckg) and FCM led to mean errors of 20% +/- 12% and 17% +/- 14%, respectively. 3-FLAB also led to more robust estimation of the maximum diameters of tumors with histology measurements, with <6% standard deviation, whereas binary FLAB, T(bckg) and FCM lead to 10%, 12%, and 13%, respectively.
  
  
  CONCLUSIONS
  These encouraging results warrant further investigation in future studies that will investigate the impact of 3-FLAB in radiotherapy treatment planning, diagnosis, and therapy response evaluation.
authors.affiliation: Institut National de la Santé et de la Recherche Médicale U650 Brest, France. hatt@univ-brest.fr
authors.forename: Mathieu
authors.initials: M
authors.lastname: Hatt
authors.forename: Catherine
authors.initials: C
authors.lastname: Cheze le Rest
authors.forename: Patrice
authors.initials: P
authors.lastname: Descourt
authors.forename: André
authors.initials: A
authors.lastname: Dekker
authors.forename: Dirk
authors.initials: D
authors.lastname: De Ruysscher
authors.forename: Michel
authors.initials: M
authors.lastname: Oellers
authors.forename: Philippe
authors.initials: P
authors.lastname: Lambin
authors.forename: Olivier
authors.initials: O
authors.lastname: Pradier
authors.forename: Dimitris
authors.initials: D
authors.lastname: Visvikis
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D019788:Fluorodeoxyglucose F18; D017143:Fuzzy Logic; D006801:Humans; D008390:Markov Chains; D008495:Medical Oncology / Q000379:methods; D009369:Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000532:radiotherapy; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted / Q000379:methods*; D047368:Tumor Burden
chemical_list: D019275:Radiopharmaceuticals; D019788:Fluorodeoxyglucose F18