Yang Long, Yang Xiong, Gong Zhenhuan, Mao Yufei, Lu Shan-Shan, Zhu Chengcheng, Wan Liwen, Huang Junhui, Mohd Noor Mohd Halim, Wu Ke, Li Cheng, Cheng Guanxun, Li Ye, Liang Dong, Liu Xin, Zheng Hairong, Hu Zhanli, Zhang Na
Lauterbur Research Center for Biomedical Imaging, Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, 518055, China.
School of Computer Sciences, University Sains Malaysia, 11800, Penang, Malaysia.
Eur Radiol. 2025 Jun 3. doi: 10.1007/s00330-025-11697-9.
To develop and validate a deep-learning-based automatic method for vessel walls and atherosclerotic plaques segmentation for quantitative evaluation in MR vessel wall images.
A total of 193 patients (107 patients for training and validation, 39 patients for internal test, 47 patients for external test) with atherosclerotic plaque from five centers underwent T1-weighted MRI scans and were included in the dataset. The first step of the proposed method was constructing a purely learning-based convolutional neural network (CNN) named Vessel-SegNet to segment the lumen and the vessel wall. The second step is using the vessel wall priors (including manual prior and Tversky-loss-based automatic prior) to improve the plaque segmentation, which utilizes the morphological similarity between the vessel wall and the plaque. The Dice similarity coefficient (DSC), intraclass correlation coefficient (ICC), etc., were used to evaluate the similarity, agreement, and correlations.
Most of the DSCs for lumen and vessel wall segmentation were above 90%. The introduction of vessel wall priors can increase the DSC for plaque segmentation by over 10%, reaching 88.45%. Compared to dice-loss-based vessel wall priors, the Tversky-loss-based priors can further improve DSC by nearly 3%, reaching 82.84%. Most of the ICC values between the Vessel-SegNet and manual methods in the 6 quantitative measurements are greater than 85% (p-value < 0.001).
The proposed CNN-based segmentation model can quickly and accurately segment vessel walls and plaques for quantitative evaluation. Due to the lack of testing with other equipment, populations, and anatomical studies, the reliability of the research results still requires further exploration.
Question How can the accuracy and efficiency of vessel component segmentation for quantification, including the lumen, vessel wall, and plaque, be improved? Findings Improved CNN models, manual/automatic vessel wall priors, and Tversky loss can improve the performance of semi-automatic/automatic vessel components segmentation for quantification. Clinical relevance Manual segmentation of vessel components is a time-consuming yet important process. Rapid and accurate segmentation of the lumen, vessel walls, and plaques for quantification assessment helps patients obtain more accurate, efficient, and timely stroke risk assessments and clinical recommendations.
开发并验证一种基于深度学习的自动方法，用于在磁共振血管壁图像中对血管壁和动脉粥样硬化斑块进行分割，以进行定量评估。
来自五个中心的193例患有动脉粥样硬化斑块的患者（107例用于训练和验证，39例用于内部测试，47例用于外部测试）接受了T1加权磁共振成像扫描，并被纳入数据集。所提出方法的第一步是构建一个名为Vessel-SegNet的纯基于学习的卷积神经网络（CNN），以分割管腔和血管壁。第二步是使用血管壁先验信息（包括手动先验和基于Tversky损失的自动先验）来改进斑块分割，该方法利用了血管壁和斑块之间的形态相似性。使用Dice相似系数（DSC）、组内相关系数（ICC）等来评估相似性、一致性和相关性。
管腔和血管壁分割的大多数DSC值均高于90%。引入血管壁先验信息可使斑块分割的DSC提高超过10%，达到88.45%。与基于骰子损失的血管壁先验信息相比，基于Tversky损失的先验信息可使DSC进一步提高近3%，达到82.84%。在6项定量测量中，Vessel-SegNet与手动方法之间的大多数ICC值均大于85%（p值<0.001）。
所提出的基于CNN的分割模型能够快速、准确地分割血管壁和斑块以进行定量评估。由于缺乏在其他设备、人群和解剖学研究中的测试，研究结果的可靠性仍需进一步探索。
问题 如何提高用于定量分析的血管成分分割的准确性和效率，包括管腔、血管壁和斑块？发现 改进的CNN模型、手动/自动血管壁先验信息和Tversky损失可提高用于定量分析的半自动/自动血管成分分割的性能。临床意义 血管成分的手动分割是一个耗时但重要的过程。快速、准确地分割管腔、血管壁和斑块以进行定量评估有助于患者获得更准确、高效和及时的中风风险评估及临床建议。
