一种用于超声图像中动脉粥样硬化颈动脉斑块分割的集成方法。

An integrated method for atherosclerotic carotid plaque segmentation in ultrasound image.

机构信息

School of Science, Nanjing University of Science and Technology, Jiangsu, China.

School of Science, Nanjing University of Science and Technology, Jiangsu, China; Department of Mathematics, Nanjing University, Jiangsu, China.

出版信息

Comput Methods Programs Biomed. 2018 Jan;153:19-32. doi: 10.1016/j.cmpb.2017.10.002. Epub 2017 Oct 3.

DOI:10.1016/j.cmpb.2017.10.002

PMID:29157451

Abstract

BACKGROUND AND OBJECTIVE

Carotid artery atherosclerosis is an important cause of stroke. Ultrasound imaging has been widely used in the diagnosis of atherosclerosis. Therefore, segmenting atherosclerotic carotid plaque in ultrasound image is an important task. Accurate plaque segmentation is helpful for the measurement of carotid plaque burden. In this paper, we propose and evaluate a novel learning-based integrated framework for plaque segmentation.

METHODS

In our study, four different classification algorithms, along with the auto-context iterative algorithm, were employed to effectively integrate features from ultrasound images and later also the iteratively estimated and refined probability maps together for pixel-wise classification. The four classification algorithms were support vector machine with linear kernel, support vector machine with radial basis function kernel, AdaBoost and random forest. The plaque segmentation was implemented in the generated probability map. The performance of the four different learning-based plaque segmentation methods was tested on 29 B-mode ultrasound images. The evaluation indices for our proposed methods were consisted of sensitivity, specificity, Dice similarity coefficient, overlap index, error of area, absolute error of area, point-to-point distance, and Hausdorff point-to-point distance, along with the area under the ROC curve.

RESULTS

The segmentation method integrated the random forest and an auto-context model obtained the best results (sensitivity 80.4 ± 8.4%, specificity 96.5 ± 2.0%, Dice similarity coefficient 81.0 ± 4.1%, overlap index 68.3 ± 5.8%, error of area -1.02 ± 18.3%, absolute error of area 14.7 ± 10.9%, point-to-point distance 0.34 ± 0.10 mm, Hausdorff point-to-point distance 1.75 ± 1.02 mm, and area under the ROC curve 0.897), which were almost the best, compared with that from the existed methods.

CONCLUSIONS

Our proposed learning-based integrated framework investigated in this study could be useful for atherosclerotic carotid plaque segmentation, which will be helpful for the measurement of carotid plaque burden.

摘要

背景与目的

颈动脉粥样硬化是中风的重要原因。超声成像是诊断动脉粥样硬化的常用方法。因此，对超声图像中的粥样硬化斑块进行分割是一项重要任务。准确的斑块分割有助于测量颈动脉斑块负担。本文提出并评估了一种新的基于学习的斑块分割综合框架。

方法

在本研究中，我们使用了四种不同的分类算法，以及自动上下文迭代算法，以有效地整合来自超声图像的特征，以及后来迭代估计和细化的概率图，以进行像素分类。这四种分类算法包括线性核支持向量机、径向基核支持向量机、AdaBoost 和随机森林。斑块分割是在生成的概率图中进行的。我们在 29 个 B 型超声图像上测试了四种不同基于学习的斑块分割方法的性能。我们提出的方法的评估指标包括敏感性、特异性、Dice 相似系数、重叠指数、面积误差、面积绝对误差、点到点距离和 Hausdorff 点到点距离，以及 ROC 曲线下的面积。

结果

集成随机森林和自动上下文模型的分割方法获得了最佳结果（敏感性 80.4±8.4%，特异性 96.5±2.0%，Dice 相似系数 81.0±4.1%，重叠指数 68.3±5.8%，面积误差-1.02±18.3%，面积绝对误差 14.7±10.9%，点到点距离 0.34±0.10 mm，Hausdorff 点到点距离 1.75±1.02 mm，ROC 曲线下的面积 0.897），与现有方法相比，几乎是最佳的。