STAPLE (Simultaneous Truth and Performance Level Estimation) is an algorithm for assessing a collection of segmentations of an image. The algorithm considers a collection of segmentations and computes a probabilistic estimate of the true segmentation and a measure of the performance level represented by each segmentation (Warfield, Zou, and Wells 2004).
The algorithm has applications beyond the assessment of segmentations, and has also been successfully applied to 1. multiple classifier fusion in brain parcellation, and 2. assessment of patterns of activation in functional MRI.

1. T Rohlfing, R Brandt, R Menzel, and C. R. Maurer, Jr. 2004. Evaluation of atlas selection strategies for atlas-based image segmentation with application to confocal microscopy images of bee brains. Neuroimage 21 (4):1428-42. [WWW]

2. T Rohlfing, D B Russakoff, and C R Maurer, Jr. 2004. Performance-based classifier combination in atlas-based image segmentation using expectation-maximization parameter estimation. IEEE Trans Med Imaging 23 (8):983-94. [WWW]

3. S K Warfield, K H Zou, and W M Wells. 2004. Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation. IEEE Trans Med Imaging 23 (7):903-21. [WWW] [PDF]

4. S K Warfield, K H Zou, and W M Wells. 2006. Validation of image segmentation by estimating rater bias and variance. Med Image Comput Comput Assist Interv Int Conf Med Image Comput Comput Assist Interv 9 (Pt 2):839-47. [WWW]

5. S K Warfield, K H Zou, and W M Wells. 2008. Validation of image segmentation by estimating rater bias and variance. Philos Transact A Math Phys Eng Sci 366 (1874):2361-75. [WWW] [PDF]