Abdomen Imaging Category

Evaluation of Five Registration Methods for the Human Abdomen on Clinically Acquired CT

Jun. 30, 2016—Zhoubing Xu, Christopher P. Lee, Marc Modat, Daniel Rueckert, Sebastien Ourselin, Richard G. Abramson, Bennett A. Landman. “Evaluation of Five Registration Methods for the Human Abdomen on Clinically Acquired CT”. IEEE Transactions of Biomedical Engineering. 2016 Aug;63(8):1563-72. PMC4972188 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27254856 Abstract Objective: This work evaluates current 3-D image registration tools on clinically acquired abdominal...

Qualitative comparisons between body-wise and organ-wise approaches on spleen (red), right kidney (yellow), and left kidney (blue). Padded bounding boxes in correspondent colors indicate the ROIs for organ-wise registrations.

Evaluation of Body-Wise and Organ-Wise Registrations for Abdominal Organs

Apr. 15, 2016—Zhoubing Xu, Sahil A. Panjwani, Christopher P. Lee, Ryan P. Burke, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman, “Evaluation of Body-Wise and Organ-Wise Registrations for Abdominal Organs”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full Text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845963/ Abstract Identifying cross-sectional and...

Abdomen and spinal cord segmentation with augmented active shape models

Feb. 27, 2016—Xu, Zhoubing; Benjamin Conrad; Rebeccah Baucom; Seth Smith; Benjamin Poulose; Landman, Bennett. “Abdomen and spinal cord segmentation with augmented active shape models.” Journal of Medical Imaging. 3(3) 036002 Full text: https://spie.org/Publications/Journal/10.1117/1.JMI.3.3.036002 Abstract The abdominal wall is an important structure differentiating subcutaneous and visceral compartments and intimately involved with maintaining abdominal structure. Segmentation of the whole...

Whole Abdominal Wall Segmentation using Augmented Active Shape Models (AASM) with Multi-Atlas Label Fusion and Level Set

Feb. 1, 2016—Zhoubing Xu, Rebeccah B. Baucom, Richard G. Abramson, Benjamin K. Poulose, Bennett A. Landman, “Whole Abdominal Wall Segmentation using Augmented Active Shape Models (AASM) with Multi-Atlas Label Fusion and Level Set”, In Proceedings of the SPIE Medical Imaging Conference. San Diego, California, February 2016. Oral presentation. Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4845968/ Abstract The abdominal wall is an...

Quantitative CT Imaging of Ventral Hernias: Preliminary Validation of an Anatomical Labeling Protocol

Oct. 31, 2015—Zhoubing Xu, Andrew J. Asman, Rebeccah Baucom, Richard G Abramson, Benjamin K. Poulose, and Bennett A. Landman. “Quantitative CT Imaging of Ventral Hernias: Preliminary Validation of an Anatomical Labeling Protocol.” PLoS ONE. 2015 Oct 28;10(10):e0141671. Full Text: https://www.ncbi.nlm.nih.gov/pubmed/26509450 Abstract OBJECTIVE: We described and validated a quantitative anatomical labeling protocol for extracting clinically relevant quantitative parameters for...

Efficient Multi-Atlas Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning

Aug. 31, 2015—Zhoubing Xu, Ryan P. Burke, Christopher P. Lee, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Efficient Multi-Atlas Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning.” Medical Image Analysis. In press May 2015. † Full Text: http://www.medicalimageanalysisjournal.com/article/S1361-8415(15)00076-6/fulltext Abstract Abdominal segmentation on clinically acquired computed tomography (CT) has been a...

The proposed general pipeline for conducting registrations and followed by metric analyses. Initially, each of 20 CT scans were pairwise linear registered using affine IRTK. Using the linear registration as a baseline, the registrations went through four non-rigid registrations. The output non-rigid registrations are evaluated against the manual segmentation via the DSC overlap, mean surface distance, and Hausdorff distance for each organ of interest. The comparison of the four non-rigid registrations was first based on all pairs of inter-subject registrations for all organs. Then another round of comparison was applied to the top 1 and top 5 registrations selected retrospectively for each target CT scan.

Evaluation of Five Image Registration Tools for Abdominal CT: Pitfalls and Opportunities with Soft Anatomy.

Feb. 12, 2015—Christopher P. Lee, Zhoubing Xu, Ryan P. Burke, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Evaluation of Five Image Registration Tools for Abdominal CT: Pitfalls and Opportunities with Soft Anatomy.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405654/ Abstract Image registration has...

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...

Flowchart of the proposed method. Given registered atlases with variable qualities, atlas selection and statistical fusion are considered as two necessary steps to obtain a reasonable fusion estimate of the target segmentation. The SIMPLE algorithm implicitly combines these two steps to fusion selected atlases; however, more information can be incorporated to improve the atlas segmentation, and a more advanced fusion technique can be used after the atlases are selected. We propose to (1) extract a probabilistic prior of the target segmentation by context learning to regularize the atlas selection in SIMPLE and (2) use Joint Label Fusion to obtain the final segmentation while characterizing of the correlated errors from among atlases.

SIMPLE Is a Good Idea (and Better with Context Learning)

Sep. 30, 2014—Zhoubing Xu, Andrew J. Asman, Peter L. Shanahan, Richard G. Abramson, Bennett A. Landman. “SIMPLE Is a Good Idea (and Better with Context Learning)”, In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Boston, MA, September 2014. Full text: PubMed Abstract Selective and iterative method for performance level estimation (SIMPLE) is a multi-atlas segmentation technique that integrates...

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...

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