Networking Research Center on Bioengineering, Biomaterials and Nanomedicine (CIBER-BBN), Barcelona 08019, Spain.
Med Phys. 2011 May;38(5):2439-49. doi: 10.1118/1.3575417.
Morphological descriptors are practical and essential biomarkers for diagnosis and treatment selection for intracranial aneurysm management according to the current guidelines in use. Nevertheless, relatively little work has been dedicated to improve the three-dimensional quantification of aneurysmal morphology, to automate the analysis, and hence to reduce the inherent intra and interobserver variability of manual analysis. In this paper we propose a methodology for the automated isolation and morphological quantification of saccular intracranial aneurysms based on a 3D representation of the vascular anatomy.
This methodology is based on the analysis of the vasculature skeleton's topology and the subsequent application of concepts from deformable cylinders. These are expanded inside the parent vessel to identify different regions and discriminate the aneurysm sac from the parent vessel wall. The method renders as output the surface representation of the isolated aneurysm sac, which can then be quantified automatically. The proposed method provides the means for identifying the aneurysm neck in a deterministic way. The results obtained by the method were assessed in two ways: they were compared to manual measurements obtained by three independent clinicians as normally done during diagnosis and to automated measurements from manually isolated aneurysms by three independent operators, nonclinicians, experts in vascular image analysis. All the measurements were obtained using in-house tools. The results were qualitatively and quantitatively compared for a set of the saccular intracranial aneurysms (n = 26).
Measurements performed on a synthetic phantom showed that the automated measurements obtained from manually isolated aneurysms where the most accurate. The differences between the measurements obtained by the clinicians and the manually isolated sacs were statistically significant (neck width: p <0.001, sac height: p = 0.002). When comparing clinicians' measurements to automatically isolated sacs, only the differences for the neck width were significant (neck width: p <0.001, sac height: p = 0.95). However, the correlation and agreement between the measurements obtained from manually and automatically isolated aneurysms for the neck width: p = 0.43 and sac height: p = 0.95 where found.
The proposed method allows the automated isolation of intracranial aneurysms, eliminating the interobserver variability. In average, the computational cost of the automated method (2 min 36 s) was similar to the time required by a manual operator (measurement by clinicians: 2 min 51 s, manual isolation: 2 min 21 s) but eliminating human interaction. The automated measurements are irrespective of the viewing angle, eliminating any bias or difference between the observer criteria. Finally, the qualitative assessment of the results showed acceptable agreement between manually and automatically isolated aneurysms.
形态学描述符是颅内动脉瘤管理中根据当前使用的指南进行诊断和治疗选择的实用且重要的生物标志物。然而，相对较少的工作致力于改善动脉瘤形态的三维定量分析，实现分析自动化，从而降低手动分析固有的观察者内和观察者间变异性。在本文中，我们提出了一种基于血管解剖三维表示的囊状颅内动脉瘤自动隔离和形态量化的方法。
该方法基于血管骨架拓扑的分析，以及随后应用可变形圆柱体的概念。这些概念在母血管内扩展，以识别不同的区域并将动脉瘤囊与母血管壁区分开来。该方法输出隔离的动脉瘤囊的表面表示，然后可以自动对其进行量化。该方法提供了以确定的方式识别动脉瘤颈部的手段。通过两种方式评估所提出方法的结果：将其与三位独立临床医生通常在诊断过程中获得的手动测量值进行比较，以及与三位非临床医生血管图像分析专家手动隔离的动脉瘤进行自动测量值进行比较。所有测量均使用内部工具获得。对一组囊状颅内动脉瘤（n=26）进行了定性和定量比较。
在合成模型上进行的测量表明，从手动隔离的动脉瘤中自动获得的测量值最准确。临床医生和手动隔离的囊之间的测量值存在统计学差异（颈部宽度：p<0.001，囊高度：p=0.002）。当将临床医生的测量值与自动隔离的囊进行比较时，只有颈部宽度的差异具有统计学意义（颈部宽度：p<0.001，囊高度：p=0.95）。然而，对于颈部宽度，手动和自动隔离的动脉瘤之间的测量值具有相关性和一致性（p=0.43），对于囊高度（p=0.95）也具有相关性和一致性。
该方法允许自动隔离颅内动脉瘤，消除观察者间的变异性。平均而言，自动方法的计算成本（2 分 36 秒）与手动操作员所需的时间（临床医生的测量：2 分 51 秒，手动隔离：2 分 21 秒）相似，但消除了人工交互。自动测量与观察角度无关，消除了观察者标准之间的任何偏差或差异。最后，对结果的定性评估表明，手动和自动隔离的动脉瘤之间具有可接受的一致性。
