CAPES Foundation, Ministry of Education of Brazil, Brasília - DF, 70040-020, Brazil; Instituto de Ciência e Inovação em Engenharia Mecânica e Engenharia Industrial, Faculdade de Engenharia, Universidade do Porto, Rua Dr. Roberto Frias, s/n, 4200-465, Porto, Portugal.
Universidade Estadual Paulista "Júlio de Mesquita Filho", Rua Cristóvão Colombo, 2265, 15054-000, S. J. do Rio Preto, Brazil.
Med Image Anal. 2017 Aug;40:60-79. doi: 10.1016/j.media.2017.06.006. Epub 2017 Jun 10.
Image assessment of the arterial system plays an important role in the diagnosis of cardiovascular diseases. The segmentation of the lumen and media-adventitia in intravascular (IVUS) images of the coronary artery is the first step towards the evaluation of the morphology of the vessel under analysis and the identification of possible atherosclerotic lesions. In this study, a fully automatic method for the segmentation of the lumen in IVUS images of the coronary artery is presented. The proposed method relies on the K-means algorithm and the mean roundness to identify the region corresponding to the potential lumen. An approach to identify and eliminate side branches on bifurcations is also proposed to delimit the area with the potential lumen regions. Additionally, an active contour model is applied to refine the contour of the lumen region. In order to evaluate the segmentation accuracy, the results of the proposed method were compared against manual delineations made by two experts in 326 IVUS images of the coronary artery. The average values of the Jaccard measure, Hausdorff distance, percentage of area difference and Dice coefficient were 0.88 ± 0.06, 0.29 ± 0.17 mm, 0.09 ± 0.07 and 0.94 ± 0.04, respectively, in 324 IVUS images successfully segmented. Additionally, a comparison with the studies found in the literature showed that the proposed method is slight better than the majority of the related methods that have been proposed. Hence, the new automatic segmentation method is shown to be effective in detecting the lumen in IVUS images without using complex solutions and user interaction.
动脉系统的图像评估在心血管疾病的诊断中起着重要作用。冠状动脉血管内超声（IVUS）图像中管腔和中膜-外膜的分割是评估分析血管形态和识别可能的动脉粥样硬化病变的第一步。本研究提出了一种冠状动脉 IVUS 图像管腔自动分割的方法。该方法依赖于 K-means 算法和平均圆度来识别潜在管腔区域。还提出了一种识别和消除分支处侧支的方法，以限定潜在管腔区域的区域。此外，应用主动轮廓模型来细化管腔区域的轮廓。为了评估分割准确性，将所提出方法的结果与两位专家在 326 张冠状动脉 IVUS 图像上的手动描绘进行了比较。在成功分割的 324 张 IVUS 图像中，Jaccard 度量、Hausdorff 距离、面积差异百分比和 Dice 系数的平均值分别为 0.88 ± 0.06、0.29 ± 0.17mm、0.09 ± 0.07 和 0.94 ± 0.04。此外，与文献中的研究进行比较表明，该方法略优于大多数已提出的相关方法。因此，新的自动分割方法无需使用复杂的解决方案和用户交互即可有效地检测 IVUS 图像中的管腔。
