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

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

TL;DR

GLIDE-Reg 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 to demonstrate its robustness across challenging downstream tasks.

Abstract

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.

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

TL;DR

GLIDE-Reg 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 to demonstrate its robustness across challenging downstream tasks.

Abstract

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.
Paper Structure (12 sections, 2 equations, 3 figures, 4 tables)

This paper contains 12 sections, 2 equations, 3 figures, 4 tables.

Table of Contents

  1. Introduction
  2. Data
  3. Method
  4. Problem Formulation
  5. Feature Extraction
  6. Dynamic Dimensionality Reduction
  7. Global-to-Local Registration
  8. Experiments
  9. Implementation Details and Evaluation Metrics
  10. Registration Results
  11. Ablation Study
  12. Conclusion

Figures (3)

  • Figure 1: GLIDE-Reg Pipeline. A pair of 3D images $I_{fix}$ and $I_{mov}$ are passed to a global feature extractor and a local feature extractor (MIND) for extracting the optimal global feature representations $GF_{fix}^*$, $GF_{mov}^*$ and local feature representations $LF_{fix}$, $LF_{mov}$, respectively. Then the features are registered with 1) feature-independent coupled convex optimization and 2) fused Adam optimization in Local-Global Registration and output the $I_{warp}$. DR stands for dimensionality reduction.
  • Figure 2: Comparison of the best performing 9 algorithms (best four in the learning-based and all five feature-based instance-optimization algorithms) on the NLST dataset.
  • Figure 3: Comparison of the feature-based instance-optimization methods on one challenging case from Lung250M, where all learning-based methods failed.