Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge
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arXiv
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| Format: | Preprint |
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2024
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| _version_ | 1866913781047623680 |
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| author | Wang, Jiacheng Chen, Xiang Hu, Renjiu Wang, Rongguang Wang, Jiazheng Liu, Min Wang, Yaonan Zhang, Hang |
| author_facet | Wang, Jiacheng Chen, Xiang Hu, Renjiu Wang, Rongguang Wang, Jiazheng Liu, Min Wang, Yaonan Zhang, Hang |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_20812 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge Wang, Jiacheng Chen, Xiang Hu, Renjiu Wang, Rongguang Wang, Jiazheng Liu, Min Wang, Yaonan Zhang, Hang Computer Vision and Pattern Recognition Machine Learning Image and Video Processing 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. |
| title | Fidelity-Imposed Displacement Editing for the Learn2Reg 2024 SHG-BF Challenge |
| topic | Computer Vision and Pattern Recognition Machine Learning Image and Video Processing |
| url | https://arxiv.org/abs/2410.20812 |