Vessel segmentation and catheter detection in X-ray angiograms using superpixels
Vessel segmentation and catheter detection in X-ray angiograms using superpixels
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DOI：
10.1007/s11517-018-1793-4
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发表时间：
2018-09-01
影响因子：
3.2
通讯作者：
Najarian, Kayvan
中科院分区：
文献类型：
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作者：
Fazlali, Hamid R.;Karimi, Nader;Najarian, Kayvan
冠状动脉疾病（CAD）是世界范围内的主要死亡原因。用于诊断CAD的最常见的成像方法之一是X射线血管造影术（XRA）。使用XRA图像进行诊断通常是具有挑战性的，这是由于一些原因，如照明不均匀、对比度低、存在其他身体组织以及存在导管。这些挑战使得诊断任务变得困难，更容易误诊。在本文中，我们提出了一种新的方法，冠状动脉分割，导管检测和中心线提取的X射线血管造影图像。对于分割，首先，利用三个不同的超像素尺度，并且确定每个超像素的血管性概率的度量。投票机制用于从三个超像素尺度获得初始分割图。通过在血管区域的每个脊线像素上寻找正交线来细化初始分割。在血管造影序列的第一帧中检测到导管，在其他帧中通过拟合二阶多项式来跟踪导管，并使用图像脊提取冠状动脉中心线。我们在两个具有挑战性的数据集上评估并比较了我们的方法与以前著名的冠状动脉分割方法之一。实验结果表明，该方法不仅可以有效地分割血管，而且可以在XRA序列中检测和跟踪导管。一般来说，心脏病专家评估的结果显示，我们提出的分割方法处理的图像中有83%被标记为良好或优秀，而比较方法的得分为48%。此外，评估结果表明，我们的方法执行速度比比较方法快67%。
Coronary artery disease (CAD) is the leading cause of death around the world. One of the most common imaging methods for diagnosing CAD is the X-ray angiography (XRA). Diagnosing using XRA images is usually challenging due to some reasons such as, non-uniform illumination, low contrast, presence of other body tissues, and presence of catheter. These challenges make the diagnosis task hard and more prone to misdiagnosis. In this paper, we propose a new method for coronary artery segmentation, catheter detection, and centerline extraction in X-ray angiography images. For the segmentation, initially, three different superpixel scales are exploited, and a measure for vesselness probability of each superpixel is determined. A voting mechanism is used for obtaining an initial segmentation map from the three superpixel scales. The initial segmentation is refined by finding the orthogonal line on each ridge pixel of vessel region. The catheter is detected in the first frame of the angiography sequence and is tracked in other frames by fitting a second order polynomial on it. Also, we use the image ridges for extracting the coronary artery centerlines. We evaluated and compared our method with one of the previous well-known coronary artery segmentation methods on two challenging datasets. The results show that our method can segment the vessels and also detect and track the catheter in the XRA sequences. In general, the results assessed by a cardiologist show that 83% of the images processed by our proposed segmentation method were labeled as good or excellent, while this score for the compared method is 48%. Also, the evaluation results show that our method performs 67% faster than the compared method.