Navaei Lavasani S, Mostaar A, Ashtiyani M
Department of Biomedical Engineering and Medical Physics, Faculty of Medicine, Shahid Beheshti University of Medical Sciences, Tehran, Iran.
J Biomed Phys Eng. 2018 Mar 1;8(1):107-116. eCollection 2018 Mar.
Dynamic contrast enhanced magnetic resonance imaging (DCE-MRI) provides functional information on the microcirculation in tissues by analyzing the enhancement kinetics which can be used as biomarkers for prostate lesions detection and characterization.
The purpose of this study is to investigate spatiotemporal patterns of tumors by extracting semi-quantitative as well as wavelet-based features, both extracted from pixel-based time-signal intensity curves to segment prostate lesions on prostate DCE-MRI.
Quantitative dynamic contrast-enhanced MRI data were acquired on 22 patients. Optimal features selected by forward selection are used for the segmentation of prostate lesions by applying fuzzy c-means (FCM) clustering. The images were reviewed by an expert radiologist and manual segmentation performed as the ground truth.
Empirical results indicate that fuzzy c-mean classifier can achieve better results in terms of sensitivity, specificity when semi-quantitative features were considered versus wavelet kinetic features for lesion segmentation (Sensitivity of 87.58% and 75.62%, respectively) and (Specificity of 89.85% and 68.89 %, respectively).
The proposed segmentation algorithm in this work can potentially be implemented for automatic prostate lesion detection in a computer aided diagnosis scheme and combined with morphologic features to increase diagnostic credibility.
动态对比增强磁共振成像（DCE-MRI）通过分析增强动力学提供组织微循环的功能信息，该增强动力学可用作前列腺病变检测和表征的生物标志物。
本研究的目的是通过提取基于像素的时间信号强度曲线的半定量特征以及基于小波的特征来研究肿瘤的时空模式，以在前列腺DCE-MRI上分割前列腺病变。
对22例患者采集定量动态对比增强MRI数据。通过前向选择选择的最佳特征用于通过应用模糊c均值（FCM）聚类来分割前列腺病变。图像由专业放射科医生进行审查，并进行手动分割作为真实情况。
实证结果表明，在考虑半定量特征与小波动力学特征进行病变分割时，模糊c均值分类器在敏感性、特异性方面能取得更好的结果（敏感性分别为87.58%和75.62%）以及（特异性分别为89.85%和68.89%）。
本工作中提出的分割算法有可能在计算机辅助诊断方案中用于自动前列腺病变检测，并与形态学特征相结合以提高诊断可信度。
