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

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  • 1Department of Radiology, University of Pennsylvania, USA.


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.

[PubMed - indexed for MEDLINE]
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