|Software name||STAPLE - Simultaneous Truth and Performance Level Estimation|
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).
|Acknowledgements||The project described was supported by Award Number R01 RR021885 from the National Center For Research Resources, and by an award from the Neuroscience Blueprint I/C through R01 EB008015 from the National Institute of Biomedical Imaging and BioEngineering.
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]
|Tutorial||A tutorial for CRKit version 1.5.2 may be downloaded here: Tutorial|
|Author||Simon K. Warfield, Ph.D.|