HosseiniPanah S, Zamani A, Emadi F, HamtaeiPour F
MSc, Department of Biomedical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
PhD, Department of Biomedical Physics and Engineering, School of Medicine, Shiraz University of Medical Sciences, Shiraz, Iran.
J Biomed Phys Eng. 2019 Dec 1;9(6):699-710. doi: 10.31661/jbpe.v0i0.986. eCollection 2019 Dec.
Multiple Sclerosis (MS) syndrome is a type of Immune-Mediated disorder in the central nervous system (CNS) which destroys myelin sheaths, and results in plaque (lesion) formation in the brain. From the clinical point of view, investigating and monitoring information such as position, volume, number, and changes of these plaques are integral parts of the controlling process this disease over a period. Visualizing MS lesions in vivo with Magnetic Resonance Imaging (MRI) has a key role in observing the course of the disease.
In this analytical study, two different processing methods were present in this study in order to make an effort to detect and localize lesions in the patients' FLAIR (Fluid-attenuated inversion recovery) images. Segmentation was performed using Ensemble Support Vector Machine (SVM) classification. The trained data was randomly divided into five equal sections, and each section was fed into the computer as an input to one of the SVM classifiers that led to five different SVM structures.
To evaluate results of segmentation, some criteria have been investigated such as Dice, Jaccard, sensitivity, specificity, PPV and accuracy. Both modes of ESVM, including first and second ones have similar results. Dice criterion was satisfied much better with specialist's work and it is observed that Dice average has 0.57±.15 and 0.6±.12 values in the first and second approach, respectively.
An acceptable overlap between those results reported by the neurologist and the ones obtained from the automatic segmentation algorithm was reached using an appropriate pre-processing in the proposed algorithm. Post-processing analysis further reduced false positives using morphological operations and also improved the evaluation criteria, including sensitivity and positive predictive value.
多发性硬化症（MS）综合征是一种中枢神经系统（CNS）的免疫介导性疾病，它会破坏髓鞘，并导致大脑中形成斑块（病变）。从临床角度来看，调查和监测这些斑块的位置、体积、数量及变化等信息是该疾病一段时间内控制过程的重要组成部分。利用磁共振成像（MRI）在体内可视化MS病变对于观察疾病进程具有关键作用。
在这项分析研究中，本研究采用了两种不同的处理方法，以便努力在患者的液体衰减反转恢复（FLAIR）图像中检测和定位病变。使用集成支持向量机（SVM）分类进行分割。将训练数据随机分为五个相等的部分，每个部分作为输入输入到计算机中，输入到导致五种不同SVM结构的其中一个SVM分类器中。
为了评估分割结果，研究了一些标准，如骰子系数、杰卡德系数、灵敏度、特异性、阳性预测值和准确率。ESVM的两种模式，包括第一种和第二种，都有相似的结果。骰子系数标准与专家的工作结果匹配得更好，并且观察到在第一种和第二种方法中，骰子系数平均值分别为0.57±0.15和0.6±0.12。
在所提出的算法中，通过适当的预处理，神经科医生报告的结果与自动分割算法获得的结果之间达到了可接受的重叠。后处理分析通过形态学操作进一步减少了假阳性，并且还改善了评估标准，包括灵敏度和阳性预测值。
