Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, Liaoning, China.
School of Computer Science and Engineering, Northeastern University, Shenyang, Liaoning, China.
Med Biol Eng Comput. 2023 Sep;61(9):2467-2480. doi: 10.1007/s11517-023-02815-0. Epub 2023 May 15.
3D vessel extraction has great significance in the diagnosis of vascular diseases. However, accurate extraction of vessels from computed tomography angiography (CTA) data is challenging. For one thing, vessels in different body parts have a wide range of scales and large curvatures; for another, the intensity distributions of vessels in different CTA data vary considerably. Besides, surrounding interfering tissue, like bones or veins with similar intensity, also seriously affects vessel extraction. Considering all the above imaging and structural features of vessels, we propose a new scale-adaptive hybrid parametric tracker (SAHPT) to extract arbitrary vessels of different body parts. First, a geometry-intensity parametric model is constructed to calculate the geometry-intensity response. While geometry parameters are calculated to adapt to the variation in scale, intensity parameters can also be estimated to meet non-uniform intensity distributions. Then, a gradient parametric model is proposed to calculate the gradient response based on a multiscale symmetric normalized gradient filter which can effectively separate the target vessel from surrounding interfering tissue. Last, a hybrid parametric model that combines the geometry-intensity and gradient parametric models is constructed to evaluate how well it fits a local image patch. In the extraction process, a multipath spherical sampling strategy is used to solve the problem of anatomical complexity. We have conducted many quantitative experiments using the synthetic and clinical CTA data, asserting its superior performance compared to traditional or deep learning-based baselines.
三维血管提取在血管疾病的诊断中具有重要意义。然而，从计算机断层血管造影（CTA）数据中准确提取血管具有挑战性。一方面，不同身体部位的血管具有广泛的尺度和较大的曲率；另一方面，不同 CTA 数据中血管的强度分布差异很大。此外，周围的干扰组织，如具有相似强度的骨骼或静脉，也严重影响血管提取。考虑到血管的所有上述成像和结构特征，我们提出了一种新的尺度自适应混合参数跟踪器（SAHPT）来提取不同身体部位的任意血管。首先，构建了一个几何-强度参数模型来计算几何-强度响应。在计算几何参数以适应尺度变化的同时，还可以估计强度参数以满足非均匀强度分布。然后，提出了一种基于多尺度对称归一化梯度滤波器的梯度参数模型来计算梯度响应，该滤波器可以有效地将目标血管与周围干扰组织分离。最后，构建了一个结合几何-强度和梯度参数模型的混合参数模型，以评估其对局部图像块的拟合程度。在提取过程中，使用多路径球形采样策略来解决解剖结构复杂的问题。我们使用合成和临床 CTA 数据进行了许多定量实验，结果表明，与传统或基于深度学习的基线相比，它具有优越的性能。
