Annu Int Conf IEEE Eng Med Biol Soc. 2022 Jul;2022:467-470. doi: 10.1109/EMBC48229.2022.9871848.
Intracranial arteries are critical blood vessels that supply the brain with oxygenated blood. Intracranial artery labels provide valuable guidance and navigation to numerous clinical applications and disease diagnoses. Various machine learning algorithms have been carried out for automation in the anatomical labeling of cerebral arteries. However, the task remains challenging because of the high complexity and variations of intracranial arteries. This study investigates a novel graph convolutional neural network with deep feature fusion for cerebral artery labeling. We introduce stacked graph convolutions in an encoder-core-decoder architecture, extracting high-level representations from graph nodes and their neighbors. Furthermore, we efficiently aggregate intermediate features from different hierarchies to enhance the proposed model's representation capability and labeling performance. We perform extensive experiments on public datasets, in which the results prove the superiority of our approach over baselines by a clear margin. Clinical relevance- The graph convolutions and feature fusion in our approach effectively extract graph information, which provides more accurate intracranial artery label predictions than existing methods and better facilitates medical research and disease diagnosis.
颅内动脉是为大脑供应含氧血液的关键血管。颅内动脉标签为众多临床应用和疾病诊断提供了有价值的指导和导航。已经针对大脑动脉的解剖学标签进行了各种机器学习算法的自动化。然而，由于颅内动脉的高度复杂性和变异性，这项任务仍然具有挑战性。本研究调查了一种新颖的图卷积神经网络，用于大脑动脉的标签。我们在编码器-核心-解码器架构中引入堆叠图卷积，从图节点及其邻居中提取高级表示。此外，我们有效地聚合来自不同层次的中间特征，以增强所提出模型的表示能力和标签性能。我们在公共数据集上进行了广泛的实验，结果清楚地证明了我们的方法相对于基线的优越性。临床相关性-我们方法中的图卷积和特征融合有效地提取了图信息，与现有方法相比，它提供了更准确的颅内动脉标签预测，并且更有利于医学研究和疾病诊断。
