论文标题
颅内动脉瘤分离和定量的血管表面分割
Vascular surface segmentation for intracranial aneurysm isolation and quantification
论文作者
论文摘要
通过量化其大小和形状，可以预测破裂风险并确定颅内动脉瘤（IAS）的最佳治疗计划（IAS）。为此，必须将IA与3D血管造影分离。 State-of-the-art methods perform IA isolation by encoding neurosurgeon's intuition about former non-dilated vessel anatomy through principled approaches like fitting a cutting plane to vasculature surface, using Gaussian curvature and vessel centerline distance constraints, by deformable contours or graph cuts guided by the curvature or restricted by Voronoi surface decomposition and similar.但是，IAS及其父脉管构型的巨大变化通常会导致失败或非直觉隔离。因此，需要手动校正，但可重复性差。在本文中，我们旨在通过两阶段深度学习的血管表面分割来提高IA隔离的准确性，鲁棒性和可重复性。表面由点云形式的局部斑块表示，这些斑点被馈入第一阶段的多层神经网络（MNN），以使描述符不变，以使点排序，旋转和比例。二元分类器作为第二阶段MNN用于分离属于IA的表面。方法验证基于57 DSA，28个CTA和5个MRA图像，其中交叉验证显示高分割敏感性为0.985，在同一数据集中对先进方法获得的0.830的大幅改善。对IA隔离的目视分析及其在CTA和DSA扫描中的高精度和可靠性一致性证实了拟议方法的临床适用性。
Predicting rupture risk and deciding on optimal treatment plan for intracranial aneurysms (IAs) is possible by quantification of their size and shape. For this purpose the IA has to be isolated from 3D angiogram. State-of-the-art methods perform IA isolation by encoding neurosurgeon's intuition about former non-dilated vessel anatomy through principled approaches like fitting a cutting plane to vasculature surface, using Gaussian curvature and vessel centerline distance constraints, by deformable contours or graph cuts guided by the curvature or restricted by Voronoi surface decomposition and similar. However, the large variability of IAs and their parent vasculature configurations often leads to failure or non-intuitive isolation. Manual corrections are thus required, but suffer from poor reproducibility. In this paper, we aim to increase the accuracy, robustness and reproducibility of IA isolation through two stage deep learning based segmentation of vascular surface. The surface was represented by local patches in form of point clouds, which were fed into first stage multilayer neural network (MNN) to obtain descriptors invariant to point ordering, rotation and scale. Binary classifier as second stage MNN was used to isolate surface belonging to the IA. Method validation was based on 57 DSA, 28 CTA and 5 MRA images, where cross-validation showed high segmentation sensitivity of 0.985, a substantial improvement over 0.830 obtained for the state-of-the-art method on the same datasets. Visual analysis of IA isolation and its high accuracy and reliability consistent across CTA and DSA scans confirmed the clinical applicability of proposed method.
