Image Segmentation Category

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 4
Rendering of the manually labeled cortical regions ().

A brain MRI atlas of the common squirrel monkey, Saimiri sciureus

Feb. 1, 2014—Yurui Gao, Shweta P. Khare, Swetasudha Panda, Ann S Choe, Iwona Stepniewska, Xia Li, Zhoahua Ding, Adam Anderson, Bennett A. Landman, “A brain MRI atlas of the common squirrel monkey, Saimiri sciureus.” In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2014. Oral Presentation. † Full Text: https://www.ncbi.nlm.nih.gov/pubmed/24817811 Abstract The common squirrel...

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...

Flowchart of the proposed iterative Non-Local STAPLE (iNLS) multi-atlas framework. Using an iterative atlas-target registration refinement framework, we expand the range of anatomical variability that can be reliably segmented.

Robust GM/WM Segmentation of the Spinal Cord with Iterative Non-Local Statistical Fusion

Oct. 30, 2013—Andrew J. Asman, Seth A. Smith, Daniel Reich, and Bennett A. Landman. “Robust GM/WM Segmentation of the Spinal Cord with Iterative Non-Local Statistical Fusion”, In Proceedings of the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). 2013 Nagoya, Japan Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3918679/ Abstract New magnetic resonance imaging (MRI) sequences are enabling clinical study of...

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...

Representative segmentations for two subjects. Labels: red: volumetric hernia, yellow: outer abdominal wall (axial), blue: inner abdominal wall (axial), green: outer abdominal wall (sagittal), pink: inner abdominal wall (sagittal), cyan: skin

Quantitative Anatomical Labeling of the Anterior Abdominal Wall

Feb. 1, 2013—Wade M. Allen, Zhoubing Xu, Andrew J. Asman, Benjamin K. Poulose, Bennett A. Landman. “Quantitative Anatomical Labeling of the Anterior Abdominal Wall.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013. Oral Presentation. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3903294/?report=classic Abstract Ventral hernias (VHs) are abnormal openings in the anterior abdominal wall that are common...

Flowchart demonstrating the PACS-based Diffusion Tensor Imaging quality analysis framework. The framework begins by utilizing a PACS-based framework for acquiring a recently scanned target. Once the DTI image is acquired, standard pre-processing steps are performed for accounting for patient movement via an intra-subject rigid registration. Next, a whole image FA map and the multi-atlas segmentation process (using the pre-computed B0 atlases) are performed simultaneously in order to achieve the pertinent information about the current subject. Finally, structure-wise FA noise estimates are constructed using a wild-bootstrap estimation procedure.

Robust Inter-Modality Multi-Atlas Segmentation for PACS-based DTI Quality Control

Feb. 1, 2013—Andrew J. Asman, Carolyn B. Lauzon, Bennett A. Landman. “Robust Inter-Modality Multi-Atlas Segmentation for PACS-based DTI Quality Control.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013. Oral Presentation. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3874255/ Abstract Anatomical contexts (spatial labels) are critical for interpretation of medical imaging content. Numerous approaches have been devised for...

Segmentation results for four subjects. The first column represents the results of bone skeleton and skin. The second column demonstrates the segmentation of outer abdominal wall (green) overlaid with grids of labeled ground truth (red). The third to the sixth columns show the intra- and inter- variability of abdominal wall over slices and the segmentation results of our approach (red).

Automatic Segmentation of Abdominal Wall in Ventral Hernia CT: A Pilot Study

Feb. 1, 2013—Zhoubing Xu, Wade M. Allen, Benjamin K. Poulose, Bennett A. Landman. “Automatic Segmentation of Abdominal Wall in Ventral Hernia CT: A Pilot Study.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2013† Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3877250/?report=classic Abstract The treatment of ventral hernias (VH) has been a challenging problem for medical care. Repair of...

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...

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