‘STAPLE’

Hierarchical representation of rater performance. A hierarchical model is developed for the brain, where, at each level, the performance of a rater is quantified. The overall quality of rater is then estimated through the unified hierarchical performance model.

Statistical label fusion with hierarchical performance models

Feb. 1, 2014—Andrew J. Asman, Alexander S. Dagley, Bennett A. Landman. “Statistical label fusion with hierarchical performance models.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014 Andrew J. Asman, Alexander S. Dagley, and Bennett A. Landman. Oral Presentation. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4013116/ Abstract Label fusion is a critical step in many image segmentation...

Non-Local Statistical Label Fusion for Multi-Atlas Segmentation.

Feb. 17, 2013—Andrew J. Asman and Bennett A. Landman. “Non-Local Statistical Label Fusion for Multi-Atlas Segmentation.” Medical Image Analysis (MEDIA). 2013. 17(2):194-208. PMC23265798 † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/23265798 Abstract: Multi-atlas segmentation provides a general purpose, fully-automated approach for transferring spatial information from an existing dataset (“atlases”) to a previously unseen context (“target”) through image registration. The method to...

Empirical experiment using axial cross-section of cerebellar data to assess the performance of STAPLE (on a slice-by-slice basis) and STAPLER (volumetric fusion). The representative slices shown in (A) – (C) present an example truth model, and observations by minimally trained undergraduate students, respectively. The slices seen in (D) and (E) are the estimated labels by STAPLE and STAPLER, respectively. The plot on the top of (F) shows the accuracy on a per slice basis of the observations (box plots) STAPLER (green), STAPLE (blue), and majority vote (red). The histogram on the bottom of (F) shows the number of observations per slice. Lastly, the plot seen in (G) shows the difference in DSC between STAPLER, STAPLE, and majority vote on a per label basis. The legend for these label numbers can be seen at the bottom of (G).

Robust Statistical Fusion of Image Labels

Feb. 1, 2012—Bennett A. Landman, Andrew J. Asman, Drew Scoggins, John A. Bogovic, Fangxu Xing, and Jerry L. Prince. “Robust Statistical Fusion of Image Labels”, IEEE Transactions on Medical Imaging. 2012 Feb;31(2):512-22. PMC3262958 Full text: https://www.ncbi.nlm.nih.gov/pubmed/22010145 Abstract Image labeling and parcellation (i.e., assigning structure to a collection of voxels) are critical tasks for the assessment of volumetric...

Finding Seeds for Segmentation Using Statistical Fusion

Feb. 1, 2012—Fangxu Xing, Andrew J. Asman, Jerry L. Prince, Bennett A. Landman. “Finding Seeds for Segmentation Using Statistical Fusion.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 Full text: https://www.ncbi.nlm.nih.gov/pubmed/23019385 Abstract Image labeling is an essential step for quantitative analysis of medical images. Many image labeling algorithms require seed identification in order...

Figure 2
Results from the controlled rater observation simulation. The target intensity image and the corresponding true labels can be seen (A) and (B), respectively. The set of input observations used in this experiment can be seen in (C). The results of the four algorithms, Majority Vote, STAPLE, Binary-COLLATE and Multi-COLLATE can be seen in (D)–(G). Note that the 3 consensus-level implementation of Multi-COLLATE is the only algorithm that gets the correct answer. The 3 energy in each of the consensus levels can be seen in (H)–(J). Note that by distributing the non-consensus voxels into two separate levels, (H) and (I), the estimation process converges to the correct answer.

Generalized Statistical Label Fusion using Multiple Consensus Levels

Feb. 1, 2012—Z. Xu, A. Asman and B. Landman. “Generalized Statistical Label Fusion using Multiple Consensus Levels.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) PMC3438516† Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=%E2%80%9CGeneralized+Statistical+Label+Fusion+using+Multiple+Consensus+Levels. Abstract Segmentation plays a critical role in exposing connections between biological structure and function. The process of label fusion collects and...

Statistical Fusion of Surface Labels provided by Multiple Raters

Mar. 1, 2010—Bogovic, B. A. Landman, P.-L. Bazin, and J. L. Prince. “Statistical Fusion of Surface Labels provided by Multiple Raters, Over-complete, and Ancillary Data”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, CA, February 2010 PMC2997739 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Statistical+Fusion+of+Surface+Labels+provided+by+Multiple+Raters Abstract Studies of the size and morphology of anatomical structures rely on accurate and reproducible delineation...

Simulations with traditional random raters. Coronal sections of the three-dimensional volume show the high resolution MRI image (A), manually drawn truth model (B), an example delineation from one random traditional rater (C), and the results of a STAPLE recombination of three label sets (D). STAPLER enables fusion of label sets when raters provide only partial datasets, but performance suffers with decreasing overlap (E). With training data (F), STAPLER improved the performance even with each rater labeling only a small portion of the dataset. Box plots in E and F show mean, quartiles, range up to 1.5σ, and outliers. The highlighted plot in E indicates the simulation for which STAPLER was equivalent to STAPLE--i.e., all raters provide a complete set of labels.

Simultaneous Truth and Performance Level Estimation with Incomplete, Over-complete, and Ancillary Data

Mar. 1, 2010—A. Landman, H. Wan, J. Bogovic, and J. L. Prince. “Simultaneous Truth and Performance Level Estimation with Incomplete, Over-complete, and Ancillary Data”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, CA, February 2010 (Oral Presentation) PMC2917119 Full Text:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC2917119/ Abstract Image labeling and parcellation are critical tasks for the assessment of volumetric and morphometric...