STATISTICAL METHODS FOR AUTOMATIC BRAIN SEGMENTATION

segmentation;MRI;automated;software;local statistics;multi-channel;Biostatistics

Pringle, James Kenneth

Johns Hopkins University

关键词: segmentation;    MRI;    automated;    software;    local statistics;    multi-channel;    Biostatistics;   

Others  :  https://jscholarship.library.jhu.edu/bitstream/handle/1774.2/37222/PRINGLE-THESIS-2014.pdf?sequence=1&isAllowed=y

瑞士|英语

来源: JOHNS HOPKINS DSpace Repository

【 摘 要 】

Background: Automatic segmentation of the brain into cerebrospinal fluid (CSF), grey matter (GM), and white matter (WM) has been of interest for over twenty years. As magnetic resonance imaging (MRI) has improved and new sequences have been developed, more and more data can be utilized to improve and accelerate segmentation algorithms.Objective: To segment the brain into CSF, GM, and WM using multichannel MRI data (T1, T2, PD, FLAIR, water image, and MTC) with a multinomial logistic regression (MLR) modeland to compare the results to software segmentations from FSL, FreeSurfer, and TOADS-CRUISE.Methods: Within each subject, the different MRI sequences are co-registered to the water image within subject. Bias field correction and intensity normalization is then applied. The aligned T1 images are used as inputs for existing automatic segmentation software. FreeSurfer and TOADS anatomical segmentations are combined into CSF, GM, and WM. Our method uses MLR applied to normalized brain images. Models are further refined byadding spline terms to model possible non-linear associations.Results: Measures of similarity—the Jaccard index, the dice index, and the confusion matrix—are presented to compare the results of existing software with those obtained from the new MLR method. Segmentations are also compared and rated by a radiology resident.Conclusions: Results based on MLR are comparable to software egmentations. In some areas, they outperform existing software.