Therenva, 4 rue Jean Jaures, 35000, Rennes, France.
Department of Cardiothoracic and Vascular Surgery, CHU Rennes, 35033, Rennes, France.
Int J Comput Assist Radiol Surg. 2017 Sep;12(9):1501-1510. doi: 10.1007/s11548-017-1591-8. Epub 2017 Apr 28.
Abdominal aortic aneurysm (AAA) is a localized, permanent and irreversible enlargement of the artery, with the formation of thrombus into the inner wall of the aneurysm. A precise patient-specific segmentation of the thrombus is useful for both the pre-operative planning to estimate the rupture risk, and for post-operative assessment to monitor the disease evolution. This paper presents a generic approach for 3D segmentation of thrombus from patients suffering from AAA using computed tomography angiography (CTA) scans.
A fast and versatile thrombus segmentation approach has been developed. It is composed of initial centerline detection and aorta lumen segmentation, an optimized pre-processing stage and the use of a 3D deformable model. The approach has been designed to be very generic and requires minimal user interaction. The proposed method was tested on different datasets with 145 patients overall, including pre- and post-operative CTAs, abdominal aorta and iliac artery sections, different calcification degrees, aneurysm sizes and contrast enhancement qualities.
The thrombus segmentation approach showed very accurate results with respect to manual delineations for all datasets ([Formula: see text] and [Formula: see text] for abdominal aorta sections on pre-operative CTA, iliac artery sections on pre-operative CTAs and aorta sections on post-operative CTA, respectively). Experiments on the different patient and image conditions showed that the method was highly versatile, with no significant differences in term of precision. Comparison with the level-set algorithm also demonstrated the superiority of the 3D deformable model. Average processing time was [Formula: see text].
We presented a near-automatic and generic thrombus segmentation algorithm applicable to a large variability of patient and imaging conditions. When integrated in an endovascular planning system, our segmentation algorithm shows its compatibility with clinical routine and could be used for pre-operative planning and post-operative assessment of endovascular procedures.
腹主动脉瘤（AAA）是动脉的局部、永久性和不可逆转的扩张，血栓形成于动脉瘤的内壁。对患者进行血栓的精确的个体化分割，对于术前规划以估计破裂风险和术后评估以监测疾病进展都非常有用。本文提出了一种基于 CT 血管造影（CTA）扫描的 AAA 患者血栓的通用 3D 分割方法。
我们开发了一种快速且通用的血栓分割方法。它由初始中心线检测和主动脉管腔分割、优化的预处理阶段以及使用 3D 可变形模型组成。该方法旨在非常通用，只需要最小的用户交互。我们的方法在总共 145 名患者的不同数据集上进行了测试，包括术前和术后 CTA、腹主动脉和髂动脉节段、不同的钙化程度、动脉瘤大小和对比增强质量。
与所有数据集的手动勾画相比，血栓分割方法的结果非常准确（术前 CTA 的腹主动脉节段[Formula: see text]和[Formula: see text]，术前 CTA 的髂动脉节段和术后 CTA 的主动脉节段）。对不同患者和图像条件的实验表明，该方法具有高度通用性，在精度方面没有显著差异。与水平集算法的比较也证明了 3D 可变形模型的优越性。平均处理时间为[Formula: see text]。
我们提出了一种接近自动和通用的血栓分割算法，适用于患者和成像条件的较大变化。当集成在内窥镜血管内治疗计划系统中时，我们的分割算法显示出与临床常规的兼容性，可用于术前规划和血管内治疗的术后评估。
