Standing on the shoulders of giants: improving medical image segmentation via bias correction

Authors:
Hongzhi Wang;Sandhitsu Das;John Pluta;Caryne Craige;Murat Altinay;Brian Avants;Michael Weiner;Susanne Mueller;Paul Yushkevich
Affiliations:
Departments of Radiology, University of Pennsylvania;Departments of Radiology, University of Pennsylvania;Departments of Radiology, University of Pennsylvania;Departments of Radiology, University of Pennsylvania;Departments of Radiology, University of Pennsylvania;Departments of Radiology, University of Pennsylvania;Department of Veterans Affairs Medical Center, San Francisco, CA;Department of Veterans Affairs Medical Center, San Francisco, CA;Departments of Radiology, University of Pennsylvania
Venue:
MICCAI'10 Proceedings of the 13th international conference on Medical image computing and computer-assisted intervention: Part III
Year:
2010

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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.