title: Standing on the shoulders of giants: improving medical image segmentation via bias correction.
abstract: We propose a simple strategy to improve automatic medical image segmentation. The key idea is that without deep understanding of a segmentation method, we can still improve its performance by directly calibrating its results with respect to manual segmentation. We formulate the calibration process as a bias correction problem, which is addressed by machine learning using training data. We apply this methodology on three segmentation problems/methods and show significant improvements for all of them.
authors.affiliation: Department of Radiology, University of Pennsylvania, USA.
authors.forename: Hongzhi
authors.initials: H
authors.lastname: Wang
authors.forename: Sandhitsu
authors.initials: S
authors.lastname: Das
authors.forename: John
authors.initials: J
authors.lastname: Pluta
authors.forename: Caryne
authors.initials: C
authors.lastname: Craige
authors.forename: Murat
authors.initials: M
authors.lastname: Altinay
authors.forename: Brian
authors.initials: B
authors.lastname: Avants
authors.forename: Michael
authors.initials: M
authors.lastname: Weiner
authors.forename: Susanne
authors.initials: S
authors.lastname: Mueller
authors.forename: Paul
authors.initials: P
authors.lastname: Yushkevich
mesh_terms: D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity