Multimodal Alignment of Histopathological Images Using Cell Segmentation and Point Set Matching for Integrative Cancer Analysis
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arXiv
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| Autori principali: | , , , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866909331307364352 |
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| author | Jiang, Jun Moore, Raymond Novotny, Brenna Liu, Leo Fogarty, Zachary Guo, Ray Svetomir, Markovic Wang, Chen |
| author_facet | Jiang, Jun Moore, Raymond Novotny, Brenna Liu, Leo Fogarty, Zachary Guo, Ray Svetomir, Markovic Wang, Chen |
| contents | Histopathological imaging is vital for cancer research and clinical practice, with multiplexed Immunofluorescence (MxIF) and Hematoxylin and Eosin (H&E) providing complementary insights. However, aligning different stains at the cell level remains a challenge due to modality differences. In this paper, we present a novel framework for multimodal image alignment using cell segmentation outcomes. By treating cells as point sets, we apply Coherent Point Drift (CPD) for initial alignment and refine it with Graph Matching (GM). Evaluated on ovarian cancer tissue microarrays (TMAs), our method achieves high alignment accuracy, enabling integration of cell-level features across modalities and generating virtual H&E images from MxIF data for enhanced clinical interpretation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2410_00152 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Multimodal Alignment of Histopathological Images Using Cell Segmentation and Point Set Matching for Integrative Cancer Analysis Jiang, Jun Moore, Raymond Novotny, Brenna Liu, Leo Fogarty, Zachary Guo, Ray Svetomir, Markovic Wang, Chen Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantitative Methods Histopathological imaging is vital for cancer research and clinical practice, with multiplexed Immunofluorescence (MxIF) and Hematoxylin and Eosin (H&E) providing complementary insights. However, aligning different stains at the cell level remains a challenge due to modality differences. In this paper, we present a novel framework for multimodal image alignment using cell segmentation outcomes. By treating cells as point sets, we apply Coherent Point Drift (CPD) for initial alignment and refine it with Graph Matching (GM). Evaluated on ovarian cancer tissue microarrays (TMAs), our method achieves high alignment accuracy, enabling integration of cell-level features across modalities and generating virtual H&E images from MxIF data for enhanced clinical interpretation. |
| title | Multimodal Alignment of Histopathological Images Using Cell Segmentation and Point Set Matching for Integrative Cancer Analysis |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Quantitative Methods |
| url | https://arxiv.org/abs/2410.00152 |