title: Comparative assessment of segmentation algorithms for tumor delineation on a test-retest [(11)C]choline dataset.
abstract: OBJECTIVE
  Many methods have been proposed for tumor segmentation from positron emission tomography images. Because of the increasingly important role that [(11)C]choline is playing in oncology and because no study has compared segmentation methods on this tracer, the authors assessed several segmentation algorithms on a [(11)C]choline test-retest dataset.
  
  
  METHODS
  Fixed and adaptive threshold-based methods, fuzzy C-means (FCM), Canny's edge detection method, the watershed transform, and the fuzzy locally adaptive Bayesian algorithm (FLAB) were used. Test-retest [(11)C]choline scans of nine patients with breast cancer were considered and the percent test-retest variability %VAR(TEST-RETEST) of tumor volume (TV) was employed to assess the results. The same methods were then applied to two denoised datasets generated by applying either a Gaussian filter or the wavelet transform.
  
  
  RESULTS
  The (semi)automated methods FCM, FLAB, and Canny emerged as the best ones in terms of TV reproducibility. For these methods, the %root mean square error %RMSE of %VAR(TEST-RETEST), defined as %RMSE= variance+mean(2), was in the range 10%-21.2%, depending on the dataset and algorithm. Threshold-based methods gave TV estimates which were extremely variable, particularly on the unsmoothed data; their performance improved on the denoised datasets, whereas smoothing did not have a remarkable impact on the (semi)automated methods. TV variability was comparable to that of SUV(MAX) and SUV(MEAN) (range 14.7%-21.9% for %RMSE of %VAR(TEST-RETEST), after the exclusion of one outlier, 40%-43% when the outlier was included).
  
  
  CONCLUSIONS
  The TV variability obtained with the best methods was similar to the one reported for TV in previous [(18)F]FDG and [(18)F]FLT studies and to the one of SUV(MAX)∕SUV(MEAN) on the authors' [(11)C]choline dataset. The good reproducibility of [(11)C]choline TV warrants further studies to test whether TV could predict early response to treatment and survival, as for [(18)F]FDG, to complement∕substitute the use of SUV(MAX) and SUV(MEAN).
authors.affiliation: Comprehensive Cancer Imaging Center, Imperial College, London, UK. gtomasi@imperial.ac.uk
authors.forename: Giampaolo
authors.initials: G
authors.lastname: Tomasi
authors.forename: Tony
authors.initials: T
authors.lastname: Shepherd
authors.forename: Federico
authors.initials: F
authors.lastname: Turkheimer
authors.forename: Dimitris
authors.initials: D
authors.lastname: Visvikis
authors.forename: Eric
authors.initials: E
authors.lastname: Aboagye
mesh_terms: D000465:Algorithms; D001185:Artificial Intelligence; D001943:Breast Neoplasms / Q000000981:diagnostic imaging*; D002250:Carbon Radioisotopes; D002794:Choline; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D049268:Positron-Emission Tomography / Q000379:methods*; D019275:Radiopharmaceuticals; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
chemical_list: D002250:Carbon Radioisotopes; D019275:Radiopharmaceuticals; D002794:Choline