Expectation Maximization Strategies for Multi-atlas Multi-label Segmentation

It is well-known in the pattern recognition community that the accuracy of classifications obtained by combining decisions made by independent classifiers can be substantially higher that the accuracy of the individual classifiers. In order to combine multiple segmentations we introduce two extensions to an expectation maximization (EM) algorithm for ground truth estimation based on multiple experts (Warfield et al., MICCAI 2002). The first method repeatedly applies the Warfield algorithm with a subsequent integration step. The second method is a multi-label extension of the Warfield algorithm. Both extensions integrate multiple segmentations into one that is closer to the unknown ground truth than the individual segmentations. In atlas-based image segmentation, multiple classifiers arise naturally by applying different registration methods to the same atlas, or the same registration method to different atlases, or both. We perform a validation study designed to quantify the success of...

• Type: Conference
• Year: 2003
• Where: IPMI
• Authors: Torsten Rohlfing, Daniel B. Russakoff, Calvin R. Maurer Jr.
• Keywords: Individual Registration-based Segmentations, Individual Segmentations, IPMI 2003, Medical Imaging, Multiple Segmentations