轻量级框架实现多区域表面配准，生成整体微分同胚变换。
NimbleReg: A light-weight deep-learning framework for diffeomorphic image registration
- 用点云表示解剖结构边界，通过PointNet实现轻量化建模。
- 在多个解剖区域表面配准上达到与主流方法相当的精度。
- 适合需要低算力、高可解释性的医学图像配准场景。
本文提出NimbleReg，一种基于多解剖区域表面表示的轻量级深度学习图像配准框架。深度学习已革新图像配准，但多数方法依赖复杂的网格表示，导致模型计算开销大。如今低成本的精细分割结果已易获取，常用于引导配准。虽已有轻量级方法以边界曲面表示分割，但缺乏将多个区域映射融合为整体微分同胚变换的机制。本研究提出的方法通过点云表征多个解剖区域表面，利用PointNet主干网络实现轻量化，同时借助微分同胚的定常速度场参数化保证变换的微分同胚性质。实验表明，该方法在配准性能上可媲美当前主流基于图像的深度学习配准技术，且计算资源消耗显著降低。
原文摘要 · Abstract (English)
This paper presents NimbleReg, a light-weight deep-learning (DL) framework for diffeomorphic image registration leveraging surface representation of multiple segmented anatomical regions. Deep learning has revolutionized image registration but most methods typically rely on cumbersome gridded representations, leading to hardware-intensive models. Reliable fine-grained segmentations, that are now accessible at low cost, are often used to guide the alignment. Light-weight methods representing segmentations in terms of boundary surfaces have been proposed, but they lack mechanism to support the fusion of multiple regional mappings into an overall diffeomorphic transformation. Building on these advances, we propose a DL registration method capable of aligning surfaces from multiple segmented regions to generate an overall diffeomorphic transformation for the whole ambient space. The proposed model is light-weight thanks to a PointNet backbone. Diffeomoprhic properties are guaranteed by taking advantage of the stationary velocity field parametrization of diffeomorphisms. We demonstrate that this approach achieves alignment comparable to state-of-the-art DL-based registration techniques that consume images.
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