Standing on the shoulders of giants: improving medical image segmentation via bias correction.
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.
Wang
Hongzhi
H
Department of Radiology, University of Pennsylvania, USA.
Das
Sandhitsu
S
Pluta
John
J
Craige
Caryne
C
Altinay
Murat
M
Avants
Brian
B
Weiner
Michael
M
Mueller
Susanne
S
Yushkevich
Paul
P
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