Head and Neck Cancer Tumor Segmentation Using Support Vector Machine in Dynamic Contrast-Enhanced MRI. (7th September 2017)

• Record Type: Journal Article
• Authors: Deng, Wei; Luo, Liangping; Lin, Xiaoyi; Fang, Tianqi; Liu, Dexiang; Dan, Guo; Chen, Hanwei
• Other Names: Xu Ziyue Academic Editor.
• Is Part Of: Contrast media & molecular imaging. Volume 2017(2017)
• Publication Date: 2017-09-07
• Subjects: Diagnostic imaging -- Periodicals; Magnetic resonance imaging -- Periodicals; Contrast media (Diagnostic imaging) -- Periodicals; Contrast Media -- Periodicals; Diagnostic Imaging -- Periodicals; Substances de contraste -- Périodiques; Diagnostics moléculaires -- Périodiques; Imagerie médicale; Substance de contraste; Périodique électronique (Descripteur de forme); Ressource Internet (Descripteur de forme); 616.0754
• DOI: 10.1155/2017/8612519
• Languages: English
• ISSNs: 1555-4309

Abstract

Abstract : Objective . We aimed to propose an automatic method based on Support Vector Machine (SVM) and Dynamic Contrast-Enhanced Magnetic Resonance Imaging (DCE-MRI) to segment the tumor lesions of head and neck cancer (HNC). Materials and Methods . 120 DCE-MRI samples were collected. Five curve features and two principal components of the normalized time-intensity curve (TIC) in 80 samples were calculated as the dataset in training three SVM classifiers. The other 40 samples were used as the testing dataset. The area overlap measure (AOM) and the corresponding ratio (CR) and percent match (PM) were calculated to evaluate the segmentation performance. The training and testing procedure was repeated for 10 times, and the average performance was calculated and compared with similar studies. Results . Our method has achieved higher accuracy compared to the previous results in literature in HNC segmentation. The average AOM with the testing dataset was 0.76 ± 0.08, and the mean CR and PM were 79 ± 9% and 86 ± 8%, respectively. Conclusion . With improved segmentation performance, our proposed method is of potential in clinical practice for HNC.