Semantic-Enhanced Feature Matching with Learnable Geometric Verification for Cross-Modal Neuron Registration
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arXiv
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| Autores principales: | , , , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866915639298359296 |
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| author | Li, Wenwei Cai, Lingyi Gong, Hui Luo, Qingming Li, Anan |
| author_facet | Li, Wenwei Cai, Lingyi Gong, Hui Luo, Qingming Li, Anan |
| contents | Accurately registering in-vivo two-photon and ex-vivo fluorescence micro-optical sectioning tomography images of individual neurons is critical for structure-function analysis in neuroscience. This task is profoundly challenging due to a significant cross-modality appearance gap, the scarcity of annotated data and severe tissue deformations. We propose a novel deep learning framework to address these issues. Our method introduces a semantic-enhanced hybrid feature descriptor, which fuses the geometric precision of local features with the contextual robustness of a vision foundation model DINOV3 to bridge the modality gap. To handle complex deformations, we replace traditional RANSAC with a learnable Geometric Consistency Confidence Module, a novel classifier trained to identify and reject physically implausible correspondences. A data-efficient two-stage training strategy, involving pre-training on synthetically deformed data and fine-tuning on limited real data, overcomes the data scarcity problem. Our framework provides a robust and accurate solution for high-precision registration in challenging biomedical imaging scenarios, enabling large-scale correlative studies. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2511_21452 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Semantic-Enhanced Feature Matching with Learnable Geometric Verification for Cross-Modal Neuron Registration Li, Wenwei Cai, Lingyi Gong, Hui Luo, Qingming Li, Anan Image and Video Processing Accurately registering in-vivo two-photon and ex-vivo fluorescence micro-optical sectioning tomography images of individual neurons is critical for structure-function analysis in neuroscience. This task is profoundly challenging due to a significant cross-modality appearance gap, the scarcity of annotated data and severe tissue deformations. We propose a novel deep learning framework to address these issues. Our method introduces a semantic-enhanced hybrid feature descriptor, which fuses the geometric precision of local features with the contextual robustness of a vision foundation model DINOV3 to bridge the modality gap. To handle complex deformations, we replace traditional RANSAC with a learnable Geometric Consistency Confidence Module, a novel classifier trained to identify and reject physically implausible correspondences. A data-efficient two-stage training strategy, involving pre-training on synthetically deformed data and fine-tuning on limited real data, overcomes the data scarcity problem. Our framework provides a robust and accurate solution for high-precision registration in challenging biomedical imaging scenarios, enabling large-scale correlative studies. |
| title | Semantic-Enhanced Feature Matching with Learnable Geometric Verification for Cross-Modal Neuron Registration |
| topic | Image and Video Processing |
| url | https://arxiv.org/abs/2511.21452 |