Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), The University of Leeds, Leeds LS2 9JT, UK.
Centre for Computational Imaging and Simulation Technologies in Biomedicine (CISTIB), The University of Leeds, Leeds LS2 9JT, UK.
Comput Methods Programs Biomed. 2023 Mar;230:107355. doi: 10.1016/j.cmpb.2023.107355. Epub 2023 Jan 15.
Automatic segmentation of the cerebral vasculature and aneurysms facilitates incidental detection of aneurysms. The assessment of aneurysm rupture risk assists with pre-operative treatment planning and enables in-silico investigation of cerebral hemodynamics within and in the vicinity of aneurysms. However, ensuring precise and robust segmentation of cerebral vessels and aneurysms in neuroimaging modalities such as three-dimensional rotational angiography (3DRA) is challenging. The vasculature constitutes a small proportion of the image volume, resulting in a large class imbalance (relative to surrounding brain tissue). Additionally, aneurysms and vessels have similar image/appearance characteristics, making it challenging to distinguish the aneurysm sac from the vessel lumen.
We propose a novel multi-class convolutional neural network to tackle these challenges and facilitate the automatic segmentation of cerebral vessels and aneurysms in 3DRA images. The proposed model is trained and evaluated on an internal multi-center dataset and an external publicly available challenge dataset.
On the internal clinical dataset, our method consistently outperformed several state-of-the-art approaches for vessel and aneurysm segmentation, achieving an average Dice score of 0.81 (0.15 higher than nnUNet) and an average surface-to-surface error of 0.20 mm (less than the in-plane resolution (0.35 mm/pixel)) for aneurysm segmentation; and an average Dice score of 0.91 and average surface-to-surface error of 0.25 mm for vessel segmentation. In 223 cases of a clinical dataset, our method accurately segmented 190 aneurysm cases.
The proposed approach can help address class imbalance problems and inter-class interference problems in multi-class segmentation. Besides, this method performs consistently on clinical datasets from four different sources and the generated results are qualified for hemodynamic simulation. Code available at https://github.com/cistib/vessel-aneurysm-segmentation.
自动分割脑血管和动脉瘤有助于偶然发现动脉瘤。评估动脉瘤破裂风险有助于术前治疗计划，并能够在颅内和动脉瘤附近进行脑血流动力学的计算机模拟研究。然而，确保在三维旋转血管造影（3DRA）等神经影像学模式中对脑血管和动脉瘤进行精确和稳健的分割具有挑战性。血管构成图像体积的一小部分，导致类不平衡（相对于周围脑组织）较大。此外，动脉瘤和血管具有相似的图像/外观特征，使得难以将动脉瘤囊与血管腔区分开来。
我们提出了一种新的多类卷积神经网络，以解决这些挑战，并促进 3DRA 图像中脑血管和动脉瘤的自动分割。所提出的模型在内部多中心数据集和外部公开可用的挑战数据集上进行训练和评估。
在内部临床数据集上，我们的方法始终优于几种血管和动脉瘤分割的最新方法，动脉瘤分割的平均 Dice 得分为 0.81（比 nnUNet 高 0.15），平均表面到表面误差为 0.20mm（小于平面内分辨率（0.35mm/像素））；血管分割的平均 Dice 得分为 0.91，平均表面到表面误差为 0.25mm。在临床数据集的 223 例病例中，我们的方法准确地分割了 190 例动脉瘤病例。
所提出的方法可以帮助解决多类分割中的类不平衡问题和类间干扰问题。此外，该方法在来自四个不同来源的临床数据集上表现一致，生成的结果可用于血流动力学模拟。代码可在 https://github.com/cistib/vessel-aneurysm-segmentation 上获得。
