Abstract
Intracranial aneurysms (IAs) are abnormal dilations of cerebral blood vessels that, if ruptured, can lead to life-threatening consequences. However, their small size and soft contrast in radiological scans often make it difficult to perform accurate and efficient detection and morphological analyses, which are critical in the clinical care of the disorder. Furthermore, the lack of large public datasets with voxel-wise expert annotations pose challenges for developing deep learning algorithms to address the issues. Therefore, we proposed a novel weakly supervised 3D multi-task UNet that integrates vesselness priors to jointly perform aneurysm detection and segmentation in time-of-flight MR angiography (TOF-MRA). Specifically, to robustly guide IA detection and segmentation, we employ the popular Frangi's vesselness filter to derive soft cerebrovascular priors for both network input and an attention block to conduct segmentation from the decoder and detection from an auxiliary branch. We train our model on the Lausanne dataset with coarse ground truth segmentation, and evaluate it on the test set with refined labels from the same database. To further assess our model's generalizability, we also validate it externally on the ADAM dataset. Our results demonstrate the superior performance of the proposed technique over the SOTA techniques for aneurysm segmentation (Dice = 0.614, 95%HD =1.38mm) and detection (false positive rate = 1.47, sensitivity = 92.9%).
Abstract (translated)
颅内动脉瘤（IAs）是脑血管的异常扩张，如果破裂，则可能导致生命危险。然而，由于其体积小且在放射学扫描中的对比度较低，准确和高效的检测及形态分析变得颇具挑战性，而这些对于该疾病的临床护理至关重要。此外，缺乏带有像素级别专家注释的大规模公开数据集也给开发深度学习算法以解决这些问题带来了困难。 因此，我们提出了一种新颖的弱监督3D多任务UNet模型，该模型结合了血管优先级信息，能够在时间飞跃磁共振血管成像（TOF-MRA）中同时执行动脉瘤检测和分割。具体来说，为了可靠地指导IA的检测和分割，我们采用了流行的Frangi血管滤波器来为网络输入和注意力模块生成软脑血管先验知识，并通过解码器进行分割，辅助分支进行检测。 我们在Lausanne数据集上使用粗略的地面真实分割对模型进行了训练，并在带有改进标签的数据库测试集中对其进行了评估。为了进一步评估我们模型的泛化能力，还在外部ADAM数据集上对其进行验证。我们的结果显示，所提出的动脉瘤分割技术（Dice = 0.614，95%HD = 1.38mm）和检测技术（假阳性率 = 1.47，灵敏度 = 92.9%）在最先进的方法中表现出了优越的性能。
URL
https://arxiv.org/abs/2508.00235
