arXiv 2603.002182026-03-03eess.IVcs.CV

GLIDE-Reg：基于协同优化基础和手工特征的全局到局部可变形配准

GLIDE-Reg: Global-to-Local Deformable Registration Using Co-Optimized Foundation and Handcrafted Features

1 Medical \& Imaging Informatics, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, USA 2 Magnetic Resonance Research Labs, Department of Radiological Sciences, David Geffen School of Medicine at UCLA, USA 3 Department of Radiation Oncology, David Geffen School of Medicine at UCLA, Los Angeles, USA

Yunzheng Zhu, Aichi Chien, Kimaya kulkarni, Luoting Zhuang, Stephen Park, Ricky Savjani, Daniel Low, William Hsu

阅读 PDF

arXiv 页面 ↗HTML 原文 ↗

AI总结：

GLIDE-Reg通过协同优化基础和手工特征，实现全局到局部的可变形配准，提升空间分辨率和解剖覆盖差异下的鲁棒性与泛化能力。

摘要：

可变形配准在医学影像中至关重要，应用于病变追踪、概率脑图生成和治疗反应评估等。然而，现有方法在空间分辨率和解剖覆盖差异方面缺乏鲁棒性和泛化能力。本文联合优化配准场和可学习降维模块，使压缩的VFM嵌入保持配准相关性，并融合这些全局语义线索与MIND局部描述子。GLIDE-Reg在两个公开队列（Lung250M和NLST）和一个机构队列（UCLA5DCT）中，6个解剖结构的平均Dice相似性系数分别为0.859、0.862和0.901，优于当前最先进的DEEDS（0.834、0.858、0.900），分别提升了3.0%、0.5%和0.1%。在目标配准误差方面，GLIDE-Reg在Lung250M地标上达到1.58 mm（相比corrField的1.25 mm和DEEDS的1.91 mm），在NLST结节中心达到1.11 mm（相比DEEDS的1.11 mm）。在结节中心的验证性能也展示了其在挑战性下游任务（如结节追踪）中的鲁棒性，这是早期肺癌诊断的关键前期步骤。

英文摘要：

Deformable registration is crucial in medical imaging. Several existing applications include lesion tracking, probabilistic atlas generation, and treatment response evaluation. However, current methods often lack robustness and generalizability across two key factors: spatial resolution and differences in anatomical coverage. We jointly optimize a registration field and a learnable dimensionality reduction module so that compressed VFM embeddings remain registration-relevant, and fuse these global semantic cues with MIND local descriptors. GLIDE-Reg achieves average dice similarity coefficients (DSC) across 6 anatomical structures of 0.859, 0.862, and 0.901 in two public cohorts (Lung250M and NLST) and one institution cohort (UCLA5DCT), and outperforms the state-of-the-art DEEDS (0.834, 0.858, 0.900) with relative improvements of 3.0%, 0.5%, and 0.1%. For target registration errors, GLIDE-Reg achieves 1.58 mm on Lung250M landmarks (compared to 1.25 mm on corrField and 1.91 mm on DEEDS) and 1.11 mm on NLST nodule centers (compared to 1.11 mm on DEEDS). The substantiated performance on the nodule centers also demonstrates its robustness across challenging downstream tasks, such as nodule tracking, which is an essential prior step for early-stage lung cancer diagnosis.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。