Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:3920-3923. doi: 10.1109/EMBC46164.2021.9630626.
Time-of-flight (TOF) magnetic resonance angiography is a non-invasive imaging modality for the diagnosis of intracranial atherosclerotic diseases (ICAD). Evaluation of the degree of the stenosis and status of posterior and anterior communicating arteries to supply enough blood flow to the distal arteries is very critical, which requires accurate evaluation of arteries. Recently, deep-learning methods have been firmly established as a robust tool in medical image segmentation, which has been resulted in developing multiple customized algorithms. For instance, BRAVE-NET, a context-based successor of U-Net-has shown promising results in MRA cerebrovascular segmentation. Another widely used context-based 3D CNN-DeepMedic-has been shown to outperform U-Net in cerebrovascular segmentation of 3D digital subtraction angiography. In this study, we aim to train and compare the two state-of-the-art deep-learning networks, BRAVE-NET and DeepMedic, for automated and reliable brain vessel segmentation from TOF-MRA images in ICAD patients. Using specially labeled data-labeled on TOF MRA and corrected on high-resolution black-blood MRI, of 51 patients with ICAD due to severe stenosis, we trained and tested both models. On an independent test dataset of 11 cases, DeepMedic slightly outperformed BRAVE-NET in terms of DSC (0.905±0.012 vs 0.893±0.015, p: 0.539) and 95HD (0.754±0.223 vs 1.768±0.609, p: 0.134), and significantly outperformed BRAVE-NET in terms of Recall (0.940±0.023 vs 0.855±0.030, p: 0.036). Qualitative assessment confirmed the superiority of DeepMedic in capturing the small and distal arteries. While BRAVE-NET consistently reported higher precision, DeepMedic generally overpredicted and could better visualize the smaller and distal arteries. In future studies, ensemble models that can leverage best of both should be developed and tested on larger datasets.Clinical Relevance- This study helps elevate the state-of-the-art for brain vessel segmentation from non-invasive MRA, which could accelerate the translation of vessel status-based biomarkers into the clinical setting.
时飞（TOF）磁共振血管造影是一种用于诊断颅内动脉粥样硬化性疾病（ICAD）的非侵入性成像方式。评估狭窄程度以及为远端动脉提供足够血流的后交通和前交通动脉的状态非常关键，这需要对动脉进行准确评估。最近，深度学习方法已牢固确立为医学图像分割的强大工具，这导致了多种定制算法的开发。例如，基于上下文的 U-Net 的后继者 BRAVE-NET，在 MRA 脑血管分割中显示出了有希望的结果。另一个广泛使用的基于上下文的 3D CNN-DeepMedic，在 3D 数字减影血管造影的脑血管分割中已被证明优于 U-Net。在这项研究中，我们旨在训练和比较两种最先进的深度学习网络，即 BRAVE-NET 和 DeepMedic，以从 ICAD 患者的 TOF-MRA 图像中自动、可靠地分割脑血管。使用专门标记的数据（基于 TOF MRA 标记并在高分辨率黑血 MRI 上校正），对 51 例因严重狭窄而导致的 ICAD 患者进行了训练和测试。在一个独立的 11 例测试数据集上，DeepMedic 在 DSC（0.905±0.012 与 0.893±0.015，p：0.539）和 95HD（0.754±0.223 与 1.768±0.609，p：0.134）方面略优于 BRAVE-NET，在召回率（0.940±0.023 与 0.855±0.030，p：0.036）方面表现出显著优势。定性评估证实了 DeepMedic 在捕获小而远端动脉方面的优势。虽然 BRAVE-NET 始终报告更高的精度，但 DeepMedic 通常会过度预测，并能更好地可视化较小和远端的动脉。在未来的研究中，应该开发并在更大的数据集上测试能够利用两者优势的集成模型。临床相关性-这项研究有助于提高非侵入性 MRA 脑血管分割的最新水平，这可能会加速基于血管状态的生物标志物向临床应用的转化。
