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Proceedings Paper

Brain MRI classification based on machine learning framework with auto-context model
Author(s): Yang Lei; Yingzi Liu; Tonghe Wang; Sibo Tian; Xue Dong; Xiaojun Jiang; Tian Liu; Hui Mao; Walter J. Curran; Hui-Kuo Shu; Xiaofeng Yang
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Paper Abstract

We propose to integrate patch-based anatomical signatures and an auto-context model into a machine learning framework to iteratively segment MRI into air, soft tissue and bone. The proposed segmentation of MRIs consists of a training stage and a segmentation stage. During the training stage, patch-based anatomical features were extracted from the aligned MRI-CT training images, and the most informative features were identified to train a serious of classification forests with auto-context model. During the segmentation stage, we extracted the selected features from the MRI and fed them into the well-trained forests for MRI segmentation. Our classified results were compared with reference CTs to quantitatively evaluate segmentation accuracy using Dice similarity coefficients (DSC). This segmentation technique was validated with a clinical study of 11 patients with both MR and CT images of the brain. The DSC for air, bone and soft-tissue were 97.79±0.76%, 93.32±2.35% and 84.49±5.50%. The corresponding CT Hounsfield units (HU) can be assigned to three segmented masks (air, soft tissue and bone) for generating the synthetic CT (SCT), which demonstrates the proposed method has promising potential in generating synthetic CT from MRI for MRI-only photon or proton radiotherapy treatment planning.

Paper Details

Date Published: 15 March 2019
PDF: 7 pages
Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109531W (15 March 2019); doi: 10.1117/12.2512555
Show Author Affiliations
Yang Lei, Winship Cancer Institute, Emory Univ. (United States)
Yingzi Liu, Winship Cancer Institute, Emory Univ. (United States)
Tonghe Wang, Winship Cancer Institute, Emory Univ. (United States)
Sibo Tian, Winship Cancer Institute, Emory Univ. (United States)
Xue Dong, Winship Cancer Institute, Emory Univ. (United States)
Xiaojun Jiang, Winship Cancer Institute, Emory Univ. (United States)
Tian Liu, Winship Cancer Institute, Emory Univ. (United States)
Hui Mao, Winship Cancer Institute, Emory Univ. (United States)
Walter J. Curran, Winship Cancer Institute, Emory Univ. (United States)
Hui-Kuo Shu, Winship Cancer Institute, Emory Univ. (United States)
Xiaofeng Yang, Winship Cancer Institute, Emory Univ. (United States)


Published in SPIE Proceedings Vol. 10953:
Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging
Barjor Gimi; Andrzej Krol, Editor(s)

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