title: Using preoperative imaging for intraoperative guidance: a case of mistaken identity.
abstract: BACKGROUND
  Surgical image guidance systems to date have tended to rely on reconstructions of preoperative datasets. This paper assesses the accuracy of these reconstructions to establish whether they are appropriate for use in image guidance platforms.
  
  
  METHODS
  Nine raters (two experts in image interpretation and preparation, three in image interpretation, and four in neither interpretation nor preparation) were asked to perform a segmentation of ten renal tumours (four cystic and six solid tumours). These segmentations were compared with a gold standard consensus segmentation generated using a previously validated algorithm.
  
  
  RESULTS
  Average sensitivity and positive predictive value (PPV) were 0.902 and 0.891, respectively. When assessing for variability between raters, significant differences were seen in the PPV, sensitivity and incursions and excursions from consensus tumour boundary.
  
  
  CONCLUSIONS
  This paper has demonstrated that the interpretation required for the segmentation of preoperative imaging of renal tumours introduces significant inconsistency and inaccuracy. Copyright © 2015 John Wiley & Sons, Ltd.
authors.affiliation: Department of Surgery and Cancer, Imperial College, London, UK.
authors.forename: Archie
authors.initials: A
authors.lastname: Hughes-Hallett
authors.affiliation: Hamlyn Centre, Institute of Global Heath Innovation, Imperial College, London, UK.
authors.forename: Philip
authors.initials: P
authors.lastname: Pratt
authors.affiliation: Department of Surgery and Cancer, Imperial College, London, UK.
authors.forename: Erik
authors.initials: E
authors.lastname: Mayer
authors.affiliation: Department of Radiology, Imperial College NHS Trust, London, UK.
authors.forename: Martin
authors.initials: M
authors.lastname: Clark
authors.affiliation: Department of Surgery and Cancer, Imperial College, London, UK.
authors.forename: Justin
authors.initials: J
authors.lastname: Vale
authors.affiliation: Department of Surgery and Cancer, Imperial College, London, UK.|Hamlyn Centre, Institute of Global Heath Innovation, Imperial College, London, UK.
authors.forename: Ara
authors.initials: A
authors.lastname: Darzi
mesh_terms: D000465:Algorithms; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D007668:Kidney / Q000000981:diagnostic imaging; D007680:Kidney Neoplasms / Q000000981:diagnostic imaging* / Q000473:pathology / Q000601:surgery*; D009392:Nephrectomy; D015588:Observer Variation; D010363:Pattern Recognition, Automated / Q000379:methods*; D011237:Predictive Value of Tests; D057234:Preoperative Period; D015203:Reproducibility of Results; D012984:Software; D025321:Surgery, Computer-Assisted / Q000379:methods*; D014057:Tomography, X-Ray Computed
keywords: augmented reality; image guided; partial nephrectomy; robotic; segmentation