title: Volumetric glioma quantification: comparison of manual and semi-automatic tumor segmentation for the quantification of tumor growth.
abstract: BACKGROUND
  Volumetric magnetic resonance imaging (MRI) is now widely available and routinely used in the evaluation of high-grade gliomas (HGGs). Ideally, volumetric measurements should be included in this evaluation. However, manual tumor segmentation is time-consuming and suffers from inter-observer variability. Thus, tools for semi-automatic tumor segmentation are needed.
  
  
  OBJECTIVE
  To present a semi-automatic method (SAM) for segmentation of HGGs and to compare this method with manual segmentation performed by experts. The inter-observer variability among experts manually segmenting HGGs using volumetric MRIs was also examined.
  
  
  METHODS
  Twenty patients with HGGs were included. All patients underwent surgical resection prior to inclusion. Each patient underwent several MRI examinations during and after adjuvant chemoradiation therapy. Three experts performed manual segmentation. The results of tumor segmentation by the experts and by the SAM were compared using Dice coefficients and kappa statistics.
  
  
  RESULTS
  A relatively close agreement was seen among two of the experts and the SAM, while the third expert disagreed considerably with the other experts and the SAM. An important reason for this disagreement was a different interpretation of contrast enhancement as either surgically-induced or glioma-induced. The time required for manual tumor segmentation was an average of 16 min per scan. Editing of the tumor masks produced by the SAM required an average of less than 2 min per sample.
  
  
  CONCLUSIONS
  Manual segmentation of HGG is very time-consuming and using the SAM could increase the efficiency of this process. However, the accuracy of the SAM ultimately depends on the expert doing the editing. Our study confirmed a considerable inter-observer variability among experts defining tumor volume from volumetric MRIs.
authors.affiliation: Department of Radiology, Stavanger University Hospital, Stavanger, Norway Faculty of Medicine, University of Oslo, Oslo, Norway audunodland@hotmail.com.
authors.forename: Audun
authors.initials: A
authors.lastname: Odland
authors.affiliation: Section of Neuroradiology, Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway.
authors.forename: Andres
authors.initials: A
authors.lastname: Server
authors.affiliation: Oncologic Unit, Section for Radiology Radiumhospitalet, Department of Radiology and Nuclear Medicine, Division of Diagnostics and Intervention, Oslo University Hospital, Oslo, Norway.
authors.forename: Cathrine
authors.initials: C
authors.lastname: Saxhaug
authors.affiliation: Department of Radiology, Sørlandet Hospital Kristiansand, Kristiansand, Norway.
authors.forename: Birger
authors.initials: B
authors.lastname: Breivik
authors.affiliation: Institute of Psychology, Faculty of Social Sciences, University of Oslo, Oslo, Norway.
authors.forename: Rasmus
authors.initials: R
authors.lastname: Groote
authors.affiliation: Faculty of Medicine, University of Oslo, Oslo, Norway The Intervention Centre, Oslo University Hospital, Oslo, Norway.
authors.forename: Jonas
authors.initials: J
authors.lastname: Vardal
authors.affiliation: The Intervention Centre, Oslo University Hospital, Oslo, Norway.
authors.forename: Christopher
authors.initials: C
authors.lastname: Larsson
authors.affiliation: The Intervention Centre, Oslo University Hospital, Oslo, Norway Department of Physics, University of Oslo, Oslo, Norway.
authors.forename: Atle
authors.initials: A
authors.lastname: Bjørnerud
mesh_terms: D000328:Adult; D000368:Aged; D001932:Brain Neoplasms / Q000473:pathology* / Q000628:therapy*; D017024:Chemotherapy, Adjuvant; D003287:Contrast Media; D005260:Female; D005910:Glioma / Q000473:pathology* / Q000628:therapy*; D006801:Humans; D021621:Imaging, Three-Dimensional; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D060787:Neoplasm Grading; D010363:Pattern Recognition, Automated; D018714:Radiotherapy, Adjuvant
chemical_list: D003287:Contrast Media
keywords: 3D computer applications; Adults; brain; central nervous system (CNS); magnetic resonance imaging (MRI); radiation therapy