Hilbert Adam, Rieger Jana, Madai Vince I, Akay Ela M, Aydin Orhun U, Behland Jonas, Khalil Ahmed A, Galinovic Ivana, Sobesky Jan, Fiebach Jochen, Livne Michelle, Frey Dietmar
Charité Lab for Artificial Intelligence in Medicine, Charité Universitätsmedizin Berlin, Berlin, Germany.
Quality | Ethics | Open Science | Translation Center for Transforming Biomedical Research, Berlin Institute of Health (BIH), Charité Universitätsmedizin Berlin, Berlin, Germany.
Front Neurol. 2022 Oct 17;13:1000914. doi: 10.3389/fneur.2022.1000914. eCollection 2022.
Brain arteries are routinely imaged in the clinical setting by various modalities, e.g., time-of-flight magnetic resonance angiography (TOF-MRA). These imaging techniques have great potential for the diagnosis of cerebrovascular disease, disease progression, and response to treatment. Currently, however, only qualitative assessment is implemented in clinical applications, relying on visual inspection. While manual or semi-automated approaches for quantification exist, such solutions are impractical in the clinical setting as they are time-consuming, involve too many processing steps, and/or neglect image intensity information. In this study, we present a deep learning-based solution for the anatomical labeling of intracranial arteries that utilizes complete information from 3D TOF-MRA images. We adapted and trained a state-of-the-art multi-scale Unet architecture using imaging data of 242 patients with cerebrovascular disease to distinguish 24 arterial segments. The proposed model utilizes vessel-specific information as well as raw image intensity information, and can thus take tissue characteristics into account. Our method yielded a performance of 0.89 macro F1 and 0.90 balanced class accuracy (bAcc) in labeling aggregated segments and 0.80 macro F1 and 0.83 bAcc in labeling detailed arterial segments on average. In particular, a higher F1 score than 0.75 for most arteries of clinical interest for cerebrovascular disease was achieved, with higher than 0.90 F1 scores in the larger, main arteries. Due to minimal pre-processing, simple usability, and fast predictions, our method could be highly applicable in the clinical setting.
在临床环境中，脑动脉通常通过多种方式进行成像，例如时间飞跃磁共振血管造影（TOF-MRA）。这些成像技术在脑血管疾病的诊断、疾病进展以及治疗反应方面具有巨大潜力。然而，目前在临床应用中仅进行定性评估，依靠目视检查。虽然存在手动或半自动的量化方法，但这些解决方案在临床环境中不实用，因为它们耗时、涉及过多处理步骤并且/或者忽略了图像强度信息。在本研究中，我们提出了一种基于深度学习的解决方案，用于颅内动脉的解剖标记，该方案利用来自3D TOF-MRA图像的完整信息。我们使用242例脑血管疾病患者的成像数据，对一种先进的多尺度Unet架构进行了调整和训练，以区分24个动脉节段。所提出的模型利用血管特定信息以及原始图像强度信息，因此可以考虑组织特征。我们的方法在标记聚合节段时平均宏F1值为0.89，平衡类准确率（bAcc）为0.90；在标记详细动脉节段时平均宏F1值为0.80，bAcc为0.83。特别是，对于大多数脑血管疾病临床关注的动脉，F1分数高于0.75，在较大的主要动脉中F1分数高于0.90。由于预处理最少、易用性强且预测速度快，我们的方法在临床环境中可能具有高度适用性。
