Du Pan, An Delin, Wang Chaoli, Wang Jian-Xun
Department of Aerospace and Mechanical Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
Department of Computer Science and Engineering, University of Notre Dame, Notre Dame, IN 46556, USA.
Sci Adv. 2025 Sep 5;11(36):eadw2825. doi: 10.1126/sciadv.adw2825.
Image-based modeling is essential for understanding cardiovascular hemodynamics and advancing the diagnosis and treatment of cardiovascular diseases. Constructing patient-specific vascular models remains labor-intensive, error-prone, and time-consuming, limiting their clinical applications. This study introduces a deep-learning framework that automates the creation of simulation-ready vascular models from medical images. The framework integrates a segmentation module for accurate voxel-based vessel delineation with a surface deformation module that performs anatomically consistent and unsupervised surface refinements guided by medical image data. The integrated pipeline addresses key limitations of existing methods, enhancing geometric accuracy and computational efficiency. Evaluated on public datasets, it achieves state-of-the-art segmentation performance while substantially reducing manual effort and processing time. The resulting vascular models exhibit anatomically accurate and visually realistic geometries, effectively capturing both primary vessels and intricate branching patterns. In conclusion, this work advances the scalability and reliability of image-based computational modeling, facilitating broader applications in clinical and research settings.
基于图像的建模对于理解心血管血流动力学以及推动心血管疾病的诊断和治疗至关重要。构建患者特异性血管模型仍然是劳动密集型、容易出错且耗时的，这限制了它们的临床应用。本研究引入了一个深度学习框架，该框架可从医学图像中自动创建可用于模拟的血管模型。该框架集成了一个分割模块，用于基于体素进行精确的血管描绘，以及一个表面变形模块，该模块在医学图像数据的引导下进行解剖学上一致且无监督的表面细化。集成管道解决了现有方法的关键局限性，提高了几何精度和计算效率。在公共数据集上进行评估时，它实现了一流的分割性能，同时大幅减少了人工工作量和处理时间。生成的血管模型呈现出解剖学上准确且视觉上逼真的几何形状，有效地捕捉了主要血管和复杂的分支模式。总之，这项工作提高了基于图像的计算建模的可扩展性和可靠性，促进了其在临床和研究环境中的更广泛应用。
