Incorporating Priors on Expert Performance Parameters for Segmentation Validation and Label Fusion: A Maximum a Posteriori STAPL

Abstract. In order to evaluate the quality of segmentations of an image and assess intra- and inter-expert variability in segmentation performance, an Expectation Maximization (EM) algorithm for Simultaneous Truth And Performance Level Estimation (STAPLE) was recently developed. This algorithm, originally presented for segmentation validation, has since been used for many applications, such as atlas construction and decision fusion. However, the manual delineation of structures of interest is a very time consuming and burdensome task. Further, as the time required and burden of manual delineation increase, the accuracy of the delineation is decreased. Therefore, it may be desirable to ask the experts to delineate only a reduced number of structures or the segmentation of all structures by all experts may simply not be achieved. Fusion from data with some structures not segmented by each expert should be carried out in a manner that accounts for the missing information. In other applica...

• Type: Journal
• Year: 2010
• Where: MICCAI
• Authors: Olivier Commowick, Simon K. Warfield
• Keywords: Algorithm, Expert, Medical Imaging, MICCAI 2010, Performance Parameters