Li Yu, Zou Liwen, Song Jiajia, Gong Kailin
School of Mathematics and Statistics, Nanjing University of Science and Technology, Nanjing 210094, China.
Department of Mathematics, Nanjing University, Nanjing 210093, China.
Bioengineering (Basel). 2024 Aug 9;11(8):812. doi: 10.3390/bioengineering11080812.
Ultrasound imaging is vital for diagnosing carotid artery vascular lesions, highlighting the importance of accurately segmenting lumens in ultrasound images to prevent, diagnose and treat vascular diseases. However, noise artifacts, blood residue and discontinuous lumens significantly affect segmentation accuracy. To achieve accurate lumen segmentation in low-quality images, we propose a novel segmentation algorithm which is guided by an adaptively generated shape prior. To tackle the above challenges, we introduce a shape-prior-based segmentation method for carotid artery lumen walls. The shape prior in this study is adaptively generated based on the evolutionary trend of vessel growth. Shape priors guide and constrain the active contour, resulting in precise segmentation. The efficacy of the proposed model was confirmed using 247 carotid artery ultrasound images, with experimental results showing an average Dice coefficient of 92.38%, demonstrating superior segmentation performance compared to existing mathematical models. Our method can quickly and effectively perform accurate lumen segmentation on low-quality carotid artery ultrasound images, which is of great significance for the diagnosis of cardiovascular and cerebrovascular diseases.
超声成像对于诊断颈动脉血管病变至关重要，凸显了在超声图像中准确分割管腔以预防、诊断和治疗血管疾病的重要性。然而，噪声伪影、血液残留和不连续的管腔会显著影响分割精度。为了在低质量图像中实现准确的管腔分割，我们提出了一种由自适应生成的形状先验引导的新型分割算法。为应对上述挑战，我们引入了一种基于形状先验的颈动脉管腔壁分割方法。本研究中的形状先验是基于血管生长的演化趋势自适应生成的。形状先验引导并约束主动轮廓，从而实现精确分割。使用247幅颈动脉超声图像证实了所提模型的有效性，实验结果显示平均Dice系数为92.38%，表明与现有数学模型相比具有更优的分割性能。我们的方法能够快速有效地对低质量颈动脉超声图像进行准确的管腔分割，这对于心血管和脑血管疾病的诊断具有重要意义。
