Author/Authors :
Deng, Wei Department of Radiology - Guangzhou Panyu Central Hospital - Guangzhou, China , Luo, Liangping First Affiliated Hospital of Jinan University - Guangzhou, China , Lin, Xiaoyi School of Biomedical Engineering - Health Science Centre - Shenzhen University - Shenzhen, China , Fang, Tianqi School of Biomedical Engineering - Health Science Centre - Shenzhen University - Shenzhen, China , Liu, Dexiang Department of Radiology - Guangzhou Panyu Central Hospital - Guangzhou, China , Dan, Guo School of Biomedical Engineering - Health Science Centre - Shenzhen University - Shenzhen, China , Chen, Hanwei Department of Radiology - Guangzhou Panyu Central Hospital - Guangzhou, China
Abstract :
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.
Keywords :
Dynamic , MRI , HNC , Machine , Tumor
