解剖学先验引导神经网络：在损失函数与架构中编码解剖学先验，并结合导丝诱导主髂动脉变形的SE(3)公式
Anatomy-Informed Neural Networks: Encoding Anatomic Priors in Loss and Architecture, with an SE(3) Formulation of Guidewire-Induced Aortoiliac Deformation
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中文总结 AI 辅助
该研究提出解剖学先验引导神经网络（AINN），结合SE(3)公式处理导丝诱导主髂动脉变形问题，通过编码解剖学先验提升模型在数据稀缺时的泛化能力，为自主血管内导航提供支撑。
中文摘要 AI 辅助
解剖学深度学习模型可能在数值上合理但解剖学上不可能，且在数据稀缺时泛化能力较差。我们提出了解剖学先验引导神经网络（Anatomy-Informed Neural Networks, AINN），其中软解剖学先验作为损失函数中的惩罚项引入（例如分支惩罚，将髂动脉上的肾移植动脉而非主动脉视为异常而非不可能），与物理信息神经网络直接类似；硬解剖学先验（例如血管连续性）则内置到架构和状态表示中，只要先验允许架构强制执行，就能从结构上使此类无效预测不可能实现。我们在一个数据有限的临床测试案例上开发了该模型：当腔内引入硬质导丝时，主髂动脉树如何变形。这对当代主动脉外科很重要，也将对自主血管内导航有重要意义。我们将血管中心线和导丝路径从R^3提升到李群SE(3)中的标架曲线，并通过单侧管腔接触不等式将Cosserat杆状导丝与经迂曲度调制、解剖学锚定的血管耦合。预测结果是耦合弹性能量的约束极小值，接触力作为其拉格朗日乘子。监督采用Wasserstein-2最优传输损失，该损失将C形臂几何投影的预测结果与观测到的血管造影图进行比较，因此可以用2D血管造影图训练3D预测。我们针对已知的真值验证了运动学、损失函数和投影；力学求解器仅针对其自身的最优性条件进行验证，且预测位移尚未达到网格收敛。此处未训练任何网络。未来工作将把该计算机模型迁移到真实CT扫描中，测试其是否能提高预测精度并减少所需的训练数据量。
英文摘要
Deep-learning models of anatomy can be numerically plausible yet anatomically impossible, and they generalize poorly when data are scarce. We introduce Anatomy-Informed Neural Networks (AINN), in which soft anatomic priors enter as penalty terms in the loss (e.g., a branching penalty that treats a renal transplant artery off the iliac instead of the aorta as unexpected rather than impossible), in direct analogy to a physics-informed neural network, and hard anatomic priors (e.g., continuity of the vessel) are built into the architecture and state representation, making such invalid predictions impossible by construction wherever the prior admits architectural enforcement. We develop it on a clinical test case with limited data: how the aortoiliac tree deforms when a stiff wire is introduced endoluminally. This is important to contemporary aortic surgery and will matter to autonomous endovascular navigation. We lift the vessel centerline and the wire path from R^3 to curves of frames in the Lie group SE(3), and couple a Cosserat-rod wire to a tortuosity-modulated, anatomically anchored vessel through a unilateral lumen-contact inequality. The prediction is a constrained minimizer of the coupled elastic energy, with contact forces as its Lagrange multipliers. Supervision is a Wasserstein-2 optimal-transport loss between the predicted projection through the C-arm geometry and the observed angiogram, so a 2D angiogram can train a 3D prediction. The kinematics, loss and projection are verified against known ground truth; the mechanics solver only against its own optimality conditions, and predicted displacement is not yet mesh-converged. Here, no network is trained. Future work will transfer this in silico model to real CT scans and test whether it improves predictive accuracy and reduces the training data required.
发表机构
- The Johns Hopkins Hospital（约翰斯·霍普金斯医院）
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