Image Segmentation Category

Multi-atlas Learner Fusion: An efficient segmentation approach for large-scale data

Dec. 26, 2015—Andrew J. Asman, Yuankai Huo, Andrew J. Plassard, and Bennett A. Landman, “Multi-atlas Learner Fusion: An efficient segmentation approach for large-scale data”, Medical Image Analysis (MedIA), 2015 Dec;26(1):82-91. Full text: http://linkinghub.elsevier.com/retrieve/pii/S1361-8415(15)00135-8 Abstract We propose multi-atlas learner fusion (MLF), a framework for rapidly and accurately replicating the highly accurate, yet computationally expensive, multi-atlas segmentation framework based on...

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...

Twelve organs of interest (left) and registration examples of variable qualities for one target image (right).

Efficient Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning

Feb. 12, 2015—Zhoubing Xu, Ryan P. Burke, Christopher P. Lee, Rebeccah B. Baucom, Benjamin K. Poulose, Richard G. Abramson, Bennett A. Landman. “Efficient Abdominal Segmentation on Clinically Acquired CT with SIMPLE Context Learning.” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405802/ Abstract Abdominal segmentation on clinically acquired computed tomography...

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...

Example renderings of (A) BrainCOLOR cortical and sub-cortical labels, (B) Eve cortical and white matter labels and (C) rectified BrainCOLOR and Eve cortical and white matter labels.

Evaluation of Atlas-Based White Matter Segmentation with Eve

Feb. 12, 2015—Andrew J. Plassard, Kendra E. Hinton, Christopher Gonzalez, Vijay Venkatraman, Susan M. Resnick, Bennett A. Landman. “Evaluation of Atlas-Based White Matter Segmentation with Eve” In Proceedings of the SPIE Medical Imaging Conference. Orlando, Florida, February 2015. † Full text: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4405655/ Abstract Multi-atlas labeling has come in wide spread use for whole brain labeling on magnetic...

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...

Qualitative results for the optimal multi-atlas segmentation approach for seven subjects are shown. For a typical subject, the top rows compare manual and automatic results for a representative 2-D slice. The bottom rows show pointwise 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 oversegmentations of the ONs can be observed in certain cases (subjects 4 and 7) supporting the results in the volumetry section

Robust Optic Nerve Segmentation on Clinically Acquired CT

Dec. 31, 2014—Robert Harrigan, Swetasudha Panda, Andrew J. Asman, Michael P. DeLisi, Benjamin C. W. Yvernault, Seth A. Smith, Robert L. Galloway, Louise A. Mawn, Bennett A. Landman “Robust Optic Nerve Segmentation on Clinically Acquired CT.” Journal of Medical Imaging. 1(3), 034006 (Dec 17, 2014). † PMC3903299 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/26158064 Abstract The optic nerve (ON) plays a...

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...

Acquired images (left) and manual labels (right) are shown for two controls (a, b), a multiple sclerosis patient (c), and an optic nerve head drusen patient (d). Axial, sagittal, and coronal views of the manually labeled optic nerves, optic chiasm, and eye globes for the subject in (b) are shown in (e).

Evaluation of Multi-Atlas Label Fusion for Orbital Segmentation on In Vivo MRI

Jul. 31, 2014—Swetasudha Panda, Andrew J Asman, Shweta P Khare, Lindsey Thompson, Louise A Mawn, Seth A Smith, Bennett A Landman. “Evaluation of Multi-Atlas Label Fusion for Orbital Segmentation on In Vivo MRI.” Journal of Medical Imaging. 1(2), 024002 (Jul–Sep 2014) † PMC4280790 Full Text: https://www.ncbi.nlm.nih.gov/pubmed/25558466 Abstract Multi-atlas methods have been successful for brain segmentation, but their application...

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