Simultaneous truth and performance level estimation (STAPLE): an algorithm for the validation of image segmentation

IEEE Transactions on Medical Imaging
Simon K WarfieldWilliam M Wells

Abstract

Characterizing the performance of image segmentation approaches has been a persistent challenge. Performance analysis is important since segmentation algorithms often have limited accuracy and precision. Interactive drawing of the desired segmentation by human raters has often been the only acceptable approach, and yet suffers from intra-rater and inter-rater variability. Automated algorithms have been sought in order to remove the variability introduced by raters, but such algorithms must be assessed to ensure they are suitable for the task. The performance of raters (human or algorithmic) generating segmentations of medical images has been difficult to quantify because of the difficulty of obtaining or estimating a known true segmentation for clinical data. Although physical and digital phantoms can be constructed for which ground truth is known or readily estimated, such phantoms do not fully reflect clinical images due to the difficulty of constructing phantoms which reproduce the full range of imaging characteristics and normal and pathological anatomical variability observed in clinical data. Comparison to a collection of segmentations by raters is an attractive alternative since it can be carried out directly on the rele...Continue Reading

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Citations

Dec 20, 2011·Neuroradiology·Xavier LladóAlex Rovira
Dec 26, 2013·Annals of Biomedical Engineering·Tingting ZhuGari D Clifford
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