Multi-atlas Segmentation Category

Intensity profiles and spatial distributions of 12 organs of interest (a–l) and a rendering of a manual segmentation (m). Note that the plotted ranges of the 12 intensity profiles are consistent, i.e., −200 ~ 400 Hounsfield unit (HU).

Multi-Atlas Segmentation for Abdominal Organs with Gaussian Mixture Models

Feb. 12, 2015—Ryan P. Burke, Zhoubing Xu, Christopher P. Lee, Rebeccah Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Multi-Atlas Segmentation for Abdominal Organs with Gaussian Mixture Models.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405670/ Abstract Abdominal organ segmentation with clinically acquired computed tomography (CT) is drawing...

Figure 3
Qualitative results for the optimal multi-atlas segmentation approach for 7 subjects are shown. For a typical subject, the top rows compare manual and automatic results for a representative 2D slice. The bottom rows show point-wise surface distance error of the label fusion estimate for the ONs and the eye globe structure. The proposed multi-atlas pipeline results in reasonably accurate segmentations for the ON structure. However, slight over segmentations of the ONs can be observed in certain cases (subjects 4 and 7).

Robust Optic Nerve Segmentation on Clinically Acquired CT

Feb. 1, 2014—Swetasudha Panda, Andrew J. Asman, Michael P. DeLis, Louise A. Mawn, Robert L. Galloway, Bennett A. Landman. “Robust Optic Nerve Segmentation on Clinically Acquired CT.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014. Oral Presentation. † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24817810 Abstract The optic nerve is a sensitive central nervous system structure,...

Figure 3
Flowchart of the proposed method. The atlas labels are co-registered to construct a pose-free implicit parametric shape model, including the mean and the modes of variation of the spleen shape. The atlas images are registered to the target image, based on which the atlas labels are propagated to the target space. The locally weighted vote yields the initial fusion result of the registered atlas labels weighted by the local intensity similarity between the registered atlas images and the target image in the form of a fuzzy estimate of label probability and a binary estimate of spleen segmentation. The binary image of the mean shape from the pre-constructed spleen shape model is registered to that of the current estimate with two distinct effective ranges, i.e., (1) the whole volume of both image and (2) the mean shape region, of the similarity metric of registration so that the pose-free shape model is transformed into the target space. The current estimate of the spleen is then projected to the two registered shape models. The shape projections are converted into probabilistic priors to adjust the label probability from locally weighted vote, and then generate a new estimate of the spleen. The estimate can be refined with iterative adjustment.

Shape-Constrained Multi-Atlas Segmentation of Spleen in CT

Feb. 1, 2014—Z. Xu, B. Li, S. Panda, A. Asman, Kristen. L. Merkle, Peter L. Shanahan, Richard G. Abramson, B. Landman. “Shape-Constrained Multi-Atlas Segmentation of Spleen in CT.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014† Full Text: https://www.ncbi.nlm.nih.gov/pubmed/?term=Shape-Constrained+Multi-Atlas+Segmentation+of+Spleen+in+CT Abstract Spleen segmentation on clinically acquired CT data is a challenging problem given...

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...

Flowchart demonstrating the proposed registration procedure. As the volumes used in this dataset are wildly varying in terms of field-of-view and voxel resolution, a robust registration procedure is needed. Our model begins by rigidly registering the bone structure of the target and the atlas images (achieved through thresholding). Using these rigid registrations the optic nerve centroids are estimated on the target image in order to estimate an appropriate region-of-interest. Using this consistent region-of-interest, consistent non-rigid registration can then be performed in order to achieve the highest possible correspondence between the targets and the atlases.

Robust Non-Local Multi-Atlas Segmentation of the Optic Nerve

Feb. 1, 2013—A. Asman, Michael P. DeLisi, Louise A. Mawn, Robert L. Galloway, and B. Landman. “Robust Non-Local Multi-Atlas Segmentation of the Optic Nerve.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013. Oral Presentation. † PMCID 24478826 Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3903299/?report=classic Abstract Labeling or segmentation of structures of interest on medical images plays an...

Example qualitative results. Three representative slices from three different brains are presented for comparison. For each example, the target, truth labels, an example atlas, the label fusion estimate and the cancer likelihood estimate are presented. Note the high quality likelihood estimate in the first two examples. On the third example, the cancer is detected, but it is not as well localized as in the first two examples.

Out-of-Atlas Labeling: A Multi-Atlas Approach to Cancer Segmentation

Dec. 31, 2012—Andrew J. Asman and Bennett A. Landman, “Out-of-Atlas Labeling: A Multi-Atlas Approach to Cancer Segmentation”, In Proceedings of the 2012 International Symposium on Biomedical Imaging (ISBI). Barcelona, Spain† Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3892947/ Abstract Conventional automated segmentation techniques for magnetic resonance imaging (MRI) fail to perform in a robust and consistent manner when brain anatomy differs...

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...

Simultaneous Segmentation and Statistical Label Fusion.

Feb. 1, 2012—A. Asman and B. Landman. “Simultaneous Segmentation and Statistical Label Fusion.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2012 (Oral Presentation) † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24357909 Abstract Labeling or segmentation of structures of interest in medical imaging plays an essential role in both clinical and scientific understanding. Two of the common...

On the Application of Human Rater Models to Statistical Fusion in Multi-Atlas Labeling

Sep. 1, 2011—A. Asman, Antong Chen, and B. Landman. “On the Application of Human Rater Models to Statistical Fusion in Multi-Atlas Labeling.” In MICCAI 2011 Workshop on Multi-Atlas Methods and Statistical Fusion. Toronto, Canada, September 2011 (Oral Presentation) NIHMS317651 Full text: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.701.3272&rep=rep1&type=pdf Abstract Segmentation is critical to understanding the complex relationships between biological structure and function. Statistical algorithms...

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