3D multi-parametric breast MRI segmentation using hierarchical support vector machine with coil sensitivity correction.

RATIONALE AND
OBJECTIVES: The goal of the study is to develop a technique to achieve accurate volumetric breast tissue segmentation using magnetic resonance imaging (MRI) data. This segmentation can be useful to aid in the diagnosis of breast cancers and to assess breast cancer risk based on breast density. Tissue segmentation is also essential for development of acoustic and thermal models used in magnetic resonance guided high-intensity focused ultrasound treatment of breast lesions.
MATERIALS AND METHODS: In addition to commonly used T1-, T2-, and proton density-weighted images, three-point Dixon water- and fat-only images were also included as part of the multiparametric inputs to a tissue segmentation algorithm using a hierarchical support vector machine (SVM). The effectiveness of a variety of preprocessing schemes was evaluated through two in vivo datasets. The performance of the hierarchical SVM was investigated and compared to the conventional classification algorithms-conventional SVM and fuzzy C-mean (FCM).
RESULTS: The need for co-registration, zero-filled interpolation, coil sensitivity correction, and optimal SNR reconstruction before the final stage classification was demonstrated. The overlap ratios of the hierarchical SVM, conventional SVM and FCM were 93.25%-94.08%, 81.68-92.28%, and 75.96%-91.02%, respectively. Classification outputs from in vivo experiments showed that the presented methodology is consistent and outperforms other algorithms.
CONCLUSION: The presented hierarchical SVM-based technique showed promising results in automatically segmenting breast tissues into fat, fibroglandular tissue, skin, and lesions. The results provide evidence that both the multiparametric breast MRI inputs and the preprocessing procedures contribute to the high accuracy of tissue classification.