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15 March 2019 Brain MRI classification based on machine learning framework with auto-context model
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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.
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Yang Lei, Yingzi Liu, Tonghe Wang, Sibo Tian, Xue Dong, Xiaojun Jiang, Tian Liu, Hui Mao, Walter J. Curran, Hui-Kuo Shu, and Xiaofeng Yang "Brain MRI classification based on machine learning framework with auto-context model", Proc. SPIE 10953, Medical Imaging 2019: Biomedical Applications in Molecular, Structural, and Functional Imaging, 109531W (15 March 2019);

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