title: Segmentation of nasopharyngeal carcinoma (NPC) lesions in MR images.
abstract: OBJECTIVE
  An accurate and reproducible method to delineate tumor margins from uninvolved tissues is of vital importance in guiding radiation therapy (RT). In nasopharyngeal carcinoma (NPC), tumor margin may be difficult to identify in magnetic resonance (MR) images, making the task of optimizing RT treatment more difficult. Our aim in this study is to develop a semiautomatic image segmentation method for NPC that requires minimal human intervention and is capable of delineating tumor margins with good accuracy and reproducibility.
  
  
  METHODS
  The segmentation algorithm includes 5 stages: masking, Bayesian probability calculation, smoothing, thresholding and seed growing, and finally dilation and overlaying of results with different thresholds. The algorithm is based on information obtained from the contrast enhancement ratio of T1-weighted images and signal intensity of T2-weighted images. The algorithm is initiated by the selection of a valid anatomical seed point within the tumor by the user. The algorithm was evaluated on MR images from 7 NPC patients and was compared against the radiologist's reference outline.
  
  
  RESULTS
  The algorithm was successfully implemented on all 7 subjects. With a threshold of 1, the average percent match is 78.5 +/- 3.86 (standard deviation) %, and the correspondence ratio is 66.5 +/- 7%.
  
  
  CONCLUSIONS
  The segmentation algorithm presented here may be useful for diagnosing NPC and may guide RT treatment planning. Further improvement will be desirable to improve the accuracy and versatility of the method.
authors.affiliation: Department of Diagnostic Radiology and Organ Imaging, Chinese University of Hong Kong, Prince of Wales Hospital, Hong Kong, China. fklee@cuhk.edu.hk
authors.forename: Francis K H
authors.initials: FK
authors.lastname: Lee
authors.forename: David K W
authors.initials: DK
authors.lastname: Yeung
authors.forename: Ann D
authors.initials: AD
authors.lastname: King
authors.forename: S F
authors.initials: SF
authors.lastname: Leung
authors.forename: Anil
authors.initials: A
authors.lastname: Ahuja
mesh_terms: D000465:Algorithms; D001499:Bayes Theorem; D003287:Contrast Media; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009303:Nasopharyngeal Neoplasms / Q000473:pathology* / Q000532:radiotherapy; D015203:Reproducibility of Results
chemical_list: D003287:Contrast Media