用元学习加速医学形状重建，推理快10倍且保持高精度
Fast Medical Shape Reconstruction via Meta-learned Implicit Neural Representations
- 通过元学习优化网络初始化参数，提升推理速度
- 在三种数据集上实现稀疏切片、不同方向的快速重建
- 可迁移至未见解剖结构，适合临床实时应用
高效快速的解剖结构重建在临床实践中至关重要。缩短检索与处理时间不仅能提升危急情况下的响应与决策效率，还支持交互式手术规划与导航。现有方法利用隐式神经函数进行医学形状重建，但泛化能力与计算速度不足，难以满足实时应用需求。为此，本文提出借助元学习优化网络参数初始化，在保持高精度的同时将推理时间降低一个数量级。我们在三个涵盖不同解剖结构与模态（CT和MRI）的公开数据集上评估该方法，结果表明模型能有效处理稀疏切片、不同方向与间距的输入配置。此外，实验验证了方法在训练时未见的形状领域仍具备强泛化能力。
原文摘要 · Abstract (English)
Efficient and fast reconstruction of anatomical structures plays a crucial role in clinical practice. Minimizing retrieval and processing times not only potentially enhances swift response and decision-making in critical scenarios but also supports interactive surgical planning and navigation. Recent methods attempt to solve the medical shape reconstruction problem by utilizing implicit neural functions. However, their performance suffers in terms of generalization and computation time, a critical metric for real-time applications. To address these challenges, we propose to leverage meta-learning to improve the network parameters initialization, reducing inference time by an order of magnitude while maintaining high accuracy. We evaluate our approach on three public datasets covering different anatomical shapes and modalities, namely CT and MRI. Our experimental results show that our model can handle various input configurations, such as sparse slices with different orientations and spacings. Additionally, we demonstrate that our method exhibits strong transferable capabilities in generalizing to shape domains unobserved at training time.
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