[1]李得瑄,王成龙,张琪,等.基于掩码建模的磁共振血管造影的三维血管分割[J].中国医学物理学杂志,2025,42(10):1361-1368.[doi:DOI:10.3969/j.issn.1005-202X.2025.10.014]
　LI Dexuan,WANG Chenglong,ZHANG Qi,et al.Three-dimensional vessel segmentation in magnetic resonance angiography using mask modeling[J].Chinese Journal of Medical Physics,2025,42(10):1361-1368.[doi:DOI:10.3969/j.issn.1005-202X.2025.10.014]
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基于掩码建模的磁共振血管造影的三维血管分割()

《中国医学物理学杂志》[ISSN:1005-202X/CN:44-1351/R]

卷:
42
期数:
2025年第10期
页码:
1361-1368
栏目:
医学影像物理
出版日期:
2025-10-29

文章信息/Info

Title:
Three-dimensional vessel segmentation in magnetic resonance angiography using mask modeling
文章编号:
1005-202X（2025）10-1361-08
作者:
李得瑄1; 王成龙1; 张琪1; 张雪凤2; 杨光1
1.华东师范大学医学磁共振与分子影像技术研究院/上海市磁共振重点实验室， 上海 200062； 2.中国人民解放军海军军医大学第一附属医院， 上海 200433
Author(s):
LI Dexuan1;  WANG Chenglong1;  ZHANG Qi1;  ZHANG Xuefeng2;  YANG Guang1
1. Shanghai Key Laboratory of Magnetic Resonance/Institute of Magnetic Resonance and Molecular Imaging in Medicine, East China Normal University, Shanghai 200062, China 2. The First Hospital Affiliated to Naval Medical University, Shanghai 200433, China
关键词:
深度学习; 血管分割; 磁共振血管成像; 拓扑连通性
Keywords:
Keywords: deep learning vessel segmentation magnetic resonance angiography topological connectivity
分类号:
R318；TP391.41
DOI:
DOI:10.3969/j.issn.1005-202X.2025.10.014
文献标志码:
A
摘要:
磁共振血管成像（MRA）是一种用于观察血管的无创成像技术。通过对MRA图像进行定量分析，可以显示血管的路径、状态和血流动态，对诊断血管病变、狭窄、阻塞等血管相关疾病具有重要意义。血管分割是血管定量分析的基础，相比其他器官的分割，血管形态复杂，难以标记，准确的三维血管标记相对稀缺，给磁共振血管造影的血管分割带来了很大的挑战。本文提出在训练血管分割模型时，采用选择性地遮挡血管的策略来增强算法捕获血管拓扑结构的能力，提升血管分割结果的连续性；同时，提出Refine网络，对分割网络的二值化分割结果进行调整，从而进一步提升分割精度。在MIDAS公开数据集的42例3D MRA数据上进行训练和测试。基于3D U-Net的基线模型，血管遮挡策略下测试集分割结果的β0 Error、β1 Error分别为1.274 2±0.210 3和0.339 3±0.081 8，比基线结果分别降低0.113 6和0.028 0。采用血管遮挡策略及Refine网络的模型，平均分割Dice达到0.710 5±0.012 5，比基线结果提升0.002 8。由此可见，本文方法可以在提升血管连通性的同时提升分割精度。
Abstract:
Abstract: Magnetic resonance angiography (MRA) is a non-invasive imaging technique used to observe blood vessels. Quantitative analysis of MRA images enables visualization of vascular pathways, condition, and blood flow dynamics, which is essential for diagnosing vascular diseases such as vascular lesions, stenosis, and occlusions. Vessel segmentation serves as the fundamental basis for quantitative vascular analysis. However, the complex morphology of vessels, difficulties in labeling, and scarcity of accurate 3D vascular annotations pose significant challenges for MRA-based vessel segmentation. A strategy of selectively occluding vessels during model training is proposed to enhance the algorithms capacity to capture the topological structure of blood vessels, thereby improving the continuity of vessel segmentation results. Additionally, a Refine network is incorporated to refine the binary segmentation results of the segmentation network, thereby further improving segmentation accuracy. Model training and testing are carried out using 42 cases of 3D MRA data from the public MIDAS dataset. For the test set, the 3D U-Net baseline model with vessel occlusion strategy shows a β0 Error of 1.274 2±0.210 3 and a β1 Error of 0.339 3±0.081 8, respectively, which are 0.113 6 and 0.028 0 lower than the baseline. The model integrating vessel occlusion strategy and Refine network achieves an average Dice score of 0.710 5 ± 0.012 5, which is 0.002 8 higher than the baseline. These results demonstrate that the proposed method effectively improves both vascular connectivity and segmentation accuracy.

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备注/Memo

备注/Memo:
【收稿日期】2025-05-15 【基金项目】上海市自然科学基金（20ZR1456300） 【作者简介】李得瑄，硕士研究生，研究方向：医学图像处理、人工智能，E-mail: 51214700071@stu.ecnu.edu.cn 【通信作者】杨光，博士，副研究员，研究方向：医学图像处理、人工智能，E-mail: gyang@phy.ecnu.edu.cn
更新日期/Last Update: 2025-10-29