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Springer, Lecture Notes in Computer Science, p. 105-112, 2010

DOI: 10.1007/978-3-642-15711-0_14

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Standing on the Shoulders of Giants: Improving Medical Image Segmentation via Bias Correction

Journal article published in 2010 by Hongzhi Wang, Sandhitsu Das, John Pluta, Caryne Craige, Murat Altinay, Brian Avants, Michael Weiner, Susanne Mueller, Paul Yushkevich ORCID
This paper is made freely available by the publisher.
This paper is made freely available by the publisher.

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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.