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Unique identifier OMICS_23533
Alternative name Simultaneous Truth And Performance Level Estimation


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Publication for Simultaneous Truth And Performance Level Estimation

STAPLE citations


Physical Exercise and Spatial Training: A Longitudinal Study of Effects on Cognition, Growth Factors, and Hippocampal Plasticity

Sci Rep
PMCID: 5844866
PMID: 29523857
DOI: 10.1038/s41598-018-19993-9

[…] ormations estimated with the symmetric normalization method (SyN) from the ANTs package. Hippocampal subfields were automatically delineated using simultaneous truth and performance level estimation (STAPLE), which estimates a probabilistic true segmentation of hippocampal subfields based on the combination of multiple atlases. Atlases were obtained through manual subfield delineation in six subje […]


Designing image segmentation studies: Statistical power, sample size and reference standard quality

Med Image Anal
PMCID: 5666910
PMID: 28772163
DOI: 10.1016/

[…] ess experienced non-clinical observer. An alternative approach is to estimate a high-quality reference standard by combining independent segmentations from multiple observers using algorithms such as STAPLE () and SIMPLE (). A third approach is to mitigate the errors in a lower-quality reference standard by increasing the sample size (, , , ). All three of these approaches, however, raise the cost […]


Tumour auto contouring on 2d cine MRI for locally advanced lung cancer: A comparative study

PMCID: 5736170
PMID: 29029832
DOI: 10.1016/j.radonc.2017.09.013

[…] The eight inter-observer cases were evaluated by comparing the STAPLE delineation with each observer using all similarity metrics ( and ). For the Hausdorff distance and the mean contour distance, the bSSFP sequence resulted in significantly lower values (p<0.05) […]


Challenges for Quality Assurance of Target Volume Delineation in Clinical Trials

PMCID: 5622143
PMID: 28993798
DOI: 10.3389/fonc.2017.00221

[…] Comparisons were usually measured against a reference contour. The definition of a reference contour varied from the contour of a recognized expert to a consensus contour with multiple observers or a Simultaneous Truth and Performance Level Estimation (STAPLE) contour () (STAPLE is the probabilistic estimate of the “true” volume generated from all observers). All these methods have an inherent def […]


Automatic Thalamus Segmentation from Magnetic Resonance Images Using Multiple Atlases Level Set Framework (MALSF)

Sci Rep
PMCID: 5487333
PMID: 28655897
DOI: 10.1038/s41598-017-04276-6

[…] To compare our results with those of recent automatic segmentation methods reported in the literature, we performed segmentation on the MICCAI data using several state-of-the-art fusion algorithms: STAPLE, Spatial STAPLE, Major Voting, Weight Voting, and SIMPLE.Table  shows the mean values and standard deviations of the similarity index for the MICCAI data. The mean similarity index of our segme […]


Deep Learning for Brain MRI Segmentation: State of the Art and Future Directions

J Digit Imaging
PMCID: 5537095
PMID: 28577131
DOI: 10.1007/s10278-017-9983-4

[…] lities of 28 ± 12% for manual segmentations of brain tumor images. To alleviate this variability, multiple expert segmentations are combined in an optimal way by using label fusion algorithms such as STAPLE [, ]. For classification tasks of brain lesions, the ground truth is obtained with biopsy and pathological tests.To evaluate performance of a newly developed deep learning approach on a task, i […]


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STAPLE institution(s)
Harvard Medical School and the Department of Radiology of Brigham and Women’s Hospital, Boston, MA, USA; Department of Radiology at Children’s Hospital, Boston, MA, USA; Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA

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