‘Multi-Atlas’

Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion

Oct. 31, 2016—Yuankai Huo, Andrew J. Asman, Andrew J. Plassard, Bennett A. Landman. “Simultaneous total intracranial volume and posterior fossa volume estimation using multi‐atlas label fusion.” Human Brain Mapping. In Press October 2016 Full text: https://www.ncbi.nlm.nih.gov/pubmed/27726243 Abstract Total intracranial volume (TICV) is an essential covariate in brain volumetric analyses. The prevalent brain imaging software packages provide automatic TICV estimates. FreeSurfer and FSL estimate TICV using a scaling factor while SPM12 accumulates probabilities of...

The large-scale cross-sectional framework on 5111 multi-site MR 3D images.

Mapping Lifetime Brain Volumetry with Covariate-Adjusted Restricted Cubic Spline Regression from Cross-sectional Multi-site MRI

Oct. 30, 2016—Yuankai Huo, Katherine Aboud, Hakmook Kang, Laurie E. Cutting, Bennett A. Landman. “Mapping Lifetime Brain Volumetry with Covariate-Adjusted Restricted Cubic Spline Regression from Cross-sectional Multi-site MRI”. In International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), Athens, Greece, October 2016. Oral Presentation. Full text: NIHMSID 826509 Abstract Understanding brain volumetry is essential to understand neurodevelopment...

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...

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...

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...

Volumetric qualitative comparison of the accuracy of the segmented gray matter for the pairwise slice-based framework, and the proposed groupwise slice-based framework. The proposed registration framework consistently estimates the complex shape of the GM horn more accurately than its pairwise counterpart. Note the different axes for the two different registration frameworks.

Groupwise Multi-Atlas Segmentation of the Spinal Cord’s Internal Structure.

Apr. 15, 2014—Andrew J. Asman, Frederick W. Bryan, Seth A. Smith, Daniel S. Reich, Bennett A. Landman. “Groupwise Multi-Atlas Segmentation of the Spinal Cord’s Internal Structure.” Medical Image Analysis (MedIA). 2014 Feb 5;18(3):460-471. PMC24556080† Full Text:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4009677/ Abstract The spinal cord is an essential and vulnerable component of the central nervous system. Differentiating and localizing the spinal...

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...

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...

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