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

摘要：

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.

作者: Hongzhi Wang Sandhitsu Das John Pluta Caryne Craige Murat Altinay Brian Avants Michael Weiner Susanne Mueller Paul Yushkevich

作者单位: Departments of Radiology, University of Pennsylvania Department of Veterans Affairs Medical Center, San Francisco, CA

会议类型: 国际会议

会议名称: The 13th International Conference on Medical Image Computing and Computer-Assisted Intervention(第13届医学影像计算与计算机辅助介入国际会议 MICCAI 2010)

会议地点: 北京

会议语种:英文

页码: 105–112

在线出版日期: 2010-09-01（万方平台首次上网日期，不代表论文的发表时间）