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Head and Neck Cancer Tumor Segmentation Using Support Vector Machine in Dynamic Contrast-Enhanced MRI 时间:2017-09-18 14:23   来源:未知   作者:admin   点击: 次 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.
1. Introduction
Head and neck cancer (HNC) is an aggressive cancer at the head and neck region with high incidence in southern China especially in Hong Kong and Guangdong [1]. Medical imaging has been very important in the diagnosis and treatment of HNC. Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) is an imaging method in which T1-weighted MRI scans are acquired dynamically after injection of MRI contrast agent, providing information about the characteristics of the physiological procedure. DCE-MRI tracks the diffusion of the contrast agent (a paramagnetic substance, normally Gadolinium-based) over time into the tissue by repeated imaging to reflect hemodynamic information such as the formation and permeability of microvascular in living tumor [2]. The DCE-MRI image stores the time-intensity curve (TIC), which is different among tissues, like cancer, normal soft tissue, bone, and so on. Compared with the traditional MRI images and CT images, the differences in DCE-MRI images among tissues are more characteristic [3].
The diagnosis and treatment of HNC require accurate tumor lesion segmentation. Regarded as the ground truth, artificial segmentation operated by experienced radiologists is nonetheless time-consuming, and the accuracy is limited by the experience of radiologists. In recent years, automatic segmentation has attracted much attention. Machine learning algorithms have been applied in the segmentation of HNC, such as supervised learning, unsupervised learning, semisupervised learning, and enhanced learning. These automatic segmentation methods may reduce the subjectivity and improve the quality in the segmentation tasks.
  Among these methods, Support Vector Machine (SVM), a supervised learning algorithm, has showed great superiority with small sample size of data [4]. In this study we aimed to develop an automatic segmentation method for HNC based on DCE-MRI by using SVM.
2. Materials and Methods
2.1. DCE-MRI Data
In our study, all subjects were recruited from The First Affiliated Hospital, Sun Yat-sen University. DCE-MRI was performed on a 3.0-T system (Magnetom Trio, Siemens) with field of view (FOV) of 22 × 22 × 6 cm (AP × RL × FH), a flip angle of 15°, and scanning time of 6 minute 47 seconds with 65 dynamic scans, 5.9 seconds per scan. The contrast agent gadodiamide Gd-DTPA (Omniscan; Nycomed, Oslo, Norway) was injected intravenously as a bolus into the blood at around the 8th dynamic acquisition using a power injector system (Spectris; Medrad, Indianola, Pennsylvania), immediately followed by a 25-mL saline flush at a rate of 3.5 mL per second. The dose of Gd-DTPA was 0.1 mmol/(kg body weight) for each patient. The reconstructed DCE-MRI images were a 4D matrix (144 × 144 × 20 × 65) with 20 slices.
  One hundred and twenty samples of DCE-MRI images containing the HNC tumor lesions were used as our database. Each sample was the DCE-MRI time series of a slice and thus was a 144 × 144 × 65 matrix. Eighty samples were selected randomly as the training dataset while the remaining 40 samples were the testing dataset to verify the accuracy of segmentation.