Medical Image Understanding and Analysis
Medical Image Understanding and Analysis
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医学图像理解与分析
DOI：
10.1007/978-3-319-60964-5_14
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发表时间：
2017
期刊：
影响因子：
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通讯作者：
Van Engelen A
中科院分区：
文献类型：
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作者：
Van Engelen A
MRI中动脉粥样硬化斑块组织成分的分割对于改善未来心血管疾病的治疗策略有希望。之前已经提出了几种方法，结果各不相同。本研究旨在对各种分类器、训练集大小和MR图像序列进行结构化比较，以确定最有前途的方法学开发策略。五种不同的分类器（线性判别分类器（LDC），二次判别分类器（QDC），随机森林（RF），和支持向量分类器与线性（SVM）和径向基函数核（SVM））进行了评估。我们使用了124例有症状患者的颈动脉MRI数据，在4个中心使用2种不同的MRI方案（45例和79例患者）进行扫描。首先，随着训练数据量的增加，准确性的学习曲线显示，在使用10 -15名患者进行训练后，性能稳定。最好的结果被发现为LDC，QDC和RF。斑块内出血是最准确的分类在这两个协议，最低的准确性被发现为富含脂质的坏死核心。其次，对于LDC和RF，研究表明，省略不同的MRI序列通常会对一个或多个类别的结果产生负面影响。然而，排除T2加权扫描并没有产生很大的影响。总之，几个分类器在MRI中的斑块成分的分类中获得了一般良好的结果。斑块内出血的识别是最有希望的，而富含脂质的坏死核心仍然是最困难的。
Segmentation of tissue components of atherosclerotic plaques in MRI is promising for improving future treatment strategies of cardiovascular diseases. Several methods have been proposed before with varying results. This study aimed to perform a structured comparison of various classifiers, training set sizes, and MR image sequences to determine the most promising strategy for methodology development. Five different classifiers (linear discriminant classifier (LDC), quadratic discriminant classifier (QDC), random forest (RF), and support vector classifiers with both a linear (SVM) and radial basis function kernel (SVM)) were evaluated. We used carotid MRI data from 124 symptomatic patients, scanned in 4 centres with 2 different MRI protocols (45 and 79 patients). Firstly, learning curves of accuracy as a function of increasing training data size showed stabilisation of performance after using10–15 patients for training. Best results were found for LDC, QDC and RF. Intraplaque haemorrhage was most accurately classified in both protocols, and lowest accuracy was found for the lipid-rich necrotic core. Secondly, for LDC and RF it was shown that leaving out different MRI sequences usually negatively affects results for one or more classes. However, leaving out T2-weighted scans did not have a big impact. In conclusion, several classifiers obtain generally good results for classification of plaque components in MRI. Identification of intraplaque haemorrhage is the most promising, and lipid-rich necrotic core remains the most difficult.