English(EN) Temporal Periodic Image Registration with Implicit Neural Representations

隐式神经表示推动周期性时序图像配准发展

作者 PulseAugur 编辑部 · [1 个来源] · 2026-10-16 04:00

研究人员开发了一种名为Temporal-IDIR的新方法，该方法利用隐式神经表示（INRs）进行周期性时序图像配准。该方法优化单个INR，以同时模拟整个图像序列中的形变，从而实现自正则化。引入了时序一致性损失来惩罚帧之间变换的差异。在DIR-LAB数据集上进行评估，Temporal-IDIR实现了1.03毫米的目标配准误差，优于其他基于INR的方法，并能在时间帧之间提供平滑插值。 AI

影响该研究引入了一种新颖的基于INR的时序图像配准方法，有望提高需要分析时间序列图像数据的应用的准确性和效率。

排序理由详细介绍一种新的图像配准方法的学术论文。[lever_c_demoted from research: ic=1 ai=1.0]

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报道来源 [1]

arXiv cs.LG TIER_1 English(EN) · Mathias Lowes, Kristine Aavild S{\o}rensen, Maxime Sermesant, Rasmus R. Paulsen · 2026-10-16 04:00

具有隐式神经表示的周期性图像时序配准

arXiv:2610.21845v1 Announce Type: cross Abstract: Implicit Neural Representations (INRs) have recently gained popularity for their ability to model functions using simple and lightweight networks. These methods have also proven effective in deformable image registration. In this …

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具有隐式神经表示的周期性图像时序配准

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