title: Skull-stripping with machine learning deformable organisms.
abstract: BACKGROUND
  Segmentation methods for medical images may not generalize well to new data sets or new tasks, hampering their utility. We attempt to remedy these issues using deformable organisms to create an easily customizable segmentation plan. We validate our framework by creating a plan to locate the brain in 3D magnetic resonance images of the head (skull-stripping).
  
  
  METHODS
  Our method borrows ideas from artificial life to govern a set of deformable models. We use control processes such as sensing, proactive planning, reactive behavior, and knowledge representation to segment an image. The image may have landmarks and features specific to that dataset; these may be easily incorporated into the plan. In addition, we use a machine learning method to make our segmentation more accurate.
  
  
  RESULTS
  Our method had the least Hausdorff distance error, but included slightly less brain voxels (false negatives). It also had the lowest false positive error and performed on par to skull-stripping specific method on other metrics.
  
  
  CONCLUSIONS
  We tested our method on 838 T1-weighted images, evaluating results using distance and overlap error metrics based on expert gold standard segmentations. We evaluated the results before and after the learning step to quantify its benefit; we also compare our results to three other widely used methods: BSE, BET, and the Hybrid Watershed algorithm.
  
  
  CONCLUSIONS
  Our framework captures diverse categories of information needed for brain segmentation and will provide a foundation for tackling a wealth of segmentation problems.
authors.affiliation: Imaging Genetics Center & Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Psychology, Stanford University, Stanford, CA, USA. Electronic address: gprasad@usc.edu.
authors.forename: Gautam
authors.initials: G
authors.lastname: Prasad
authors.affiliation: Signal and Image Processing Institute, USC, Los Angeles, CA, USA.
authors.forename: Anand A
authors.initials: AA
authors.lastname: Joshi
authors.affiliation: Imaging Genetics Center & Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, CA, USA.
authors.forename: Albert
authors.initials: A
authors.lastname: Feng
authors.affiliation: Imaging Genetics Center & Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Ophthalmology, Neurology, Psychiatry & Behavioral Sciences, Radiology, and Biomedical Engineering, Keck School of Medicine of USC, Los Angeles, CA, USA.
authors.forename: Arthur W
authors.initials: AW
authors.lastname: Toga
authors.affiliation: Imaging Genetics Center & Laboratory of Neuro Imaging, Institute for Neuroimaging and Informatics, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Ophthalmology, Neurology, Psychiatry & Behavioral Sciences, Radiology, and Biomedical Engineering, Keck School of Medicine of USC, Los Angeles, CA, USA; Department of Pediatrics, Keck School of Medicine of USC, Los Angeles, CA, USA.
authors.forename: Paul M
authors.initials: PM
authors.lastname: Thompson
authors.affiliation: Department of Computer Science, UCLA, Los Angeles, CA, USA.
authors.forename: Demetri
authors.initials: D
authors.lastname: Terzopoulos
mesh_terms: D000328:Adult; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001185:Artificial Intelligence; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology; D060825:Cognitive Dysfunction / Q000473:pathology; D005189:False Positive Reactions; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008279:Magnetic Resonance Imaging / Q000379:methods; D008954:Models, Biological; D012886:Skull / Q000033:anatomy & histology* / Q000473:pathology; D014427:Twins; D055815:Young Adult
keywords: Adaboost; Deformable organisms; Hausdorff; MRI; Overlap; Registration; Skull-stripping