Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge
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Abstract (arXiv)
Co-examination of second-harmonic generation (SHG) and bright-field (BF) microscopy enables the differentiation of tissue components and collagen fibers, aiding the analysis of human breast and pancreatic cancer tissues. However, large discrepancies between SHG and BF images pose challenges for current learning-based registration models in aligning SHG to BF. In this paper, we propose a novel multi-modal registration framework that employs fidelity-imposed displacement editing to address these challenges. The framework integrates batch-wise contrastive learning, feature-based pre-alignment, and instance-level optimization. Experimental results from the Learn2Reg COMULISglobe SHG-BF Challenge validate the effectiveness of our method, securing the 1st place on the online leaderboard.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 15 of its 28 references that are in the map. First posted on arXiv 28 Oct 2024.
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References · 15
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- 2024XFeat: Accelerated Features for Lightweight Image Matchingcited by 113
- 2020CoMIR: Contrastive Multimodal Image Representation for Registrationcited by 45
- 2021Fast 3D registration with accurate optimisation and little learning for Learn2Reg 2021cited by 40
- 2022Affine Medical Image Registration with Coarse-to-Fine Vision Transformercited by 35
- 2019GLAMpoints: Greedily Learned Accurate Match pointscited by 30
- 2023Spatially Covariant Image Registration with Text Promptscited by 18
- 2024MemWarp: Discontinuity-Preserving Cardiac Registration with Memorized Anatomical Filterscited by 13
- 2021Fast computation of mutual information in the frequency domain with applications to global multimodal image alignmentcited by 6
3 more
- 2024Slicer Networkscited by 6
- 2023Novel OCT mosaicking pipeline with Feature- and Pixel-based registrationcited by 5
- 2024Retinal IPA: Iterative KeyPoints Alignment for Multimodal Retinal Imagingcited by 3
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