title: Fully automatic segmentation of the brain from T1-weighted MRI using Bridge Burner algorithm.
abstract: OBJECTIVE
  To validate Bridge Burner, a new brain segmentation algorithm based on thresholding, connectivity, surface detection, and a new operator of constrained growing.
  
  
  METHODS
  T1-weighted MR images were selected at random from three previous neuroimaging studies to represent a spectrum of system manufacturers, pulse sequences, subject ages, genders, and neurological conditions. The ground truth consisted of brain masks generated manually by a consensus of expert observers. All cases were segmented using a common set of parameters.
  
  
  RESULTS
  Bridge Burner segmentation errors were 3.4% +/- 1.3% (volume mismatch) and 0.34 +/- 0.17 mm (surface mismatch). The disagreement among experts was 3.8% +/- 2.0% (volume mismatch) and 0.48 +/- 0.49 mm (surface mismatch). The error obtained using the brain extraction tool (BET), a widely used brain segmentation program, was 8.3% +/- 9.1%. Bridge Burner brain masks are visually similar to the masks generated by human experts. Areas affected by signal intensity nonuniformity artifacts were occasionally undersegmented, and meninges and large sinuses were often falsely classified as the brain tissue. Segmentation of one MRI dataset takes seven seconds.
  
  
  CONCLUSIONS
  The new fully automatic algorithm appears to provide accurate brain segmentation from high-resolution T1-weighted MR images.
authors.affiliation: Department of Radiology, New York University School of Medicine, New York, New York 10016, USA.
authors.forename: Artem
authors.initials: A
authors.lastname: Mikheev
authors.forename: Gregory
authors.initials: G
authors.lastname: Nevsky
authors.forename: Siddharth
authors.initials: S
authors.lastname: Govindan
authors.forename: Robert
authors.initials: R
authors.lastname: Grossman
authors.forename: Henry
authors.initials: H
authors.lastname: Rusinek
mesh_terms: D000293:Adolescent; D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000473:pathology*; D001927:Brain Diseases / Q000175:diagnosis*; D001931:Brain Mapping / Q000379:methods*; D016022:Case-Control Studies; D005260:Female; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D008875:Middle Aged; D015588:Observer Variation; D015203:Reproducibility of Results; D012986:Software Validation; D013997:Time Factors