title: Evaluation of an automatic segmentation algorithm for definition of head and neck organs at risk.
abstract: BACKGROUND
  The accurate definition of organs at risk (OARs) is required to fully exploit the benefits of intensity-modulated radiotherapy (IMRT) for head and neck cancer. However, manual delineation is time-consuming and there is considerable inter-observer variability. This is pertinent as function-sparing and adaptive IMRT have increased the number and frequency of delineation of OARs. We evaluated accuracy and potential time-saving of Smart Probabilistic Image Contouring Engine (SPICE) automatic segmentation to define OARs for salivary-, swallowing- and cochlea-sparing IMRT.
  
  
  METHODS
  Five clinicians recorded the time to delineate five organs at risk (parotid glands, submandibular glands, larynx, pharyngeal constrictor muscles and cochleae) for each of 10 CT scans. SPICE was then used to define these structures. The acceptability of SPICE contours was initially determined by visual inspection and the total time to modify them recorded per scan. The Simultaneous Truth and Performance Level Estimation (STAPLE) algorithm created a reference standard from all clinician contours. Clinician, SPICE and modified contours were compared against STAPLE by the Dice similarity coefficient (DSC) and mean/maximum distance to agreement (DTA).
  
  
  RESULTS
  For all investigated structures, SPICE contours were less accurate than manual contours. However, for parotid/submandibular glands they were acceptable (median DSC: 0.79/0.80; mean, maximum DTA: 1.5 mm, 14.8 mm/0.6 mm, 5.7 mm). Modified SPICE contours were also less accurate than manual contours. The utilisation of SPICE did not result in time-saving/improve efficiency.
  
  
  CONCLUSIONS
  Improvements in accuracy of automatic segmentation for head and neck OARs would be worthwhile and are required before its routine clinical implementation.
authors.forename: David
authors.initials: D
authors.lastname: Thomson
authors.forename: Chris
authors.initials: C
authors.lastname: Boylan
authors.forename: Tom
authors.initials: T
authors.lastname: Liptrot
authors.forename: Adam
authors.initials: A
authors.lastname: Aitkenhead
authors.forename: Lip
authors.initials: L
authors.lastname: Lee
authors.forename: Beng
authors.initials: B
authors.lastname: Yap
authors.forename: Andrew
authors.initials: A
authors.lastname: Sykes
authors.forename: Carl
authors.initials: C
authors.lastname: Rowbottom
authors.affiliation: Department of Clinical Oncology, The Christie NHS Foundation Trust, Wilmslow Road, Manchester M20 4BX, UK. Nick.Slevin@christie.nhs.uk.
authors.forename: Nicholas
authors.initials: N
authors.lastname: Slevin
mesh_terms: D000465:Algorithms; D001330:Electronic Data Processing; D006258:Head and Neck Neoplasms / Q000000981:diagnostic imaging* / Q000532:radiotherapy; D006801:Humans; D015588:Observer Variation; D058958:Organs at Risk / Q000000981:diagnostic imaging* / Q000528:radiation effects; D011857:Radiographic Image Interpretation, Computer-Assisted; D011879:Radiotherapy Dosage; D011880:Radiotherapy Planning, Computer-Assisted; D050397:Radiotherapy, Intensity-Modulated; D014057:Tomography, X-Ray Computed
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