Automatic Optimization of Segmentation Algorithms Through Simultaneous Truth and Performance Level Estimation (STAPLE)

The performance of automatic segmentation algorithms often depends critically upon a number of parameters intrinsic to the algorithm. Appropriate setting of these parameters is a pre-requisite for successful segmentation, and yet may be difficult for users to achieve. We propose here a novel algorithm for the automatic selection of optimal parameters for medical image segmentation. Our algorithm makes use of STAPLE (Simultaneous Truth and Performance Level Estimation), a previously described and validated algorithm for automatically identifying a reference standard by which to assess segmentation generators. We execute a set of independent automated segmentation algorithms with initial parameter settings, on a set of images from any clinical application under consideration, estimate a reference standard from the segmentation results using STAPLE, and then identify the parameter settings for each algorithm that maximizes the quality of the segmentation generator result with respect to the reference standard. The process of estimating a reference standard and estimating the optimal parameter settings is iterated to convergence.