‘Abdomen Segmentation’

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...

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...

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...