Estimation of inferential uncertainty in assessing expert segmentation performance from STAPLE.
The evaluation of the quality of segmentations of an image, and the assessment of intra- and inter-expert variability in segmentation performance, has long been recognized as a difficult task. For a segmentation validation task, it may be effective to compare the results of an automatic segmentation algorithm to multiple expert segmentations. Recently an expectation-maximization (EM) algorithm for simultaneous truth and performance level estimation (STAPLE) was developed to this end to compute both an estimate of the reference standard segmentation and performance parameters from a set of segmentations of an image. The performance is characterized by the rate of detection of each segmentation label by each expert in comparison to the estimated reference standard. This previous work provides estimates of performance parameters,but does not provide any information regarding the uncertainty of the estimated values. An estimate of this inferential uncertainty, if available, would allow the estimation of confidence intervals for the values of the parameters. This would facilitate the interpretation of the performance of segmentation generators and help determine if sufficient data size and number of segmentations have been obtained to precisely characterize the performance parameters. We present a new algorithm to estimate the inferential uncertainty of the performance parameters for binary and multi-category segmentations. It is derived for the special case of the STAPLE algorithm based on established theory for general purpose covariance matrix estimation for EM algorithms. The bounds on the performance parameters are estimated by the computation of the observed information matrix.We use this algorithm to study the bounds on performance parameters estimates from simulated images with specified performance parameters, and from interactive segmentations of neonatal brain MRIs. We demonstrate that confidence intervals for expert segmentation performance parameters can be estimated with our algorithm. We investigate the influence of the number of experts and of the segmented data size on these bounds, showing that it is possible to determine the number of image segmentations and the size of images necessary to achieve a chosen level of accuracy in segmentation performance assessment.
Commowick
Olivier
O
Computational Radiology Laboratory, Department of Radiology, Children's Hospital, Boston, MA 02115, USA. olivier.commowick@childrens.harvard.edu
Warfield
Simon K
SK
D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology; D003198:Computer Simulation; D016001:Confidence Intervals; D016208:Databases, Factual; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D007231:Infant, Newborn; D008279:Magnetic Resonance Imaging / Q000379:methods*; D015203:Reproducibility of Results