A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis
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| Main Authors: | , , , , , , , |
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| Format: | Preprint |
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2023
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| _version_ | 1866917613389479936 |
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| author | Zhao, Linxi Tang, Jiankai Chen, Dongyu Liu, Xiaohong Zhou, Yong Shi, Yuanchun Wang, Guangyu Wang, Yuntao |
| author_facet | Zhao, Linxi Tang, Jiankai Chen, Dongyu Liu, Xiaohong Zhou, Yong Shi, Yuanchun Wang, Guangyu Wang, Yuntao |
| contents | Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotations as supervised labels and integrated them into a novel end-to-end nailfold capillary analysis pipeline. This pipeline excels in automatically detecting and measuring a wide range of size factors, morphological features, and dynamic aspects of nailfold capillaries. We compared our outcomes with clinical reports. Experiment results showed that our automated pipeline achieves an average of sub-pixel level precision in measurements and 89.9% accuracy in identifying morphological abnormalities. These results underscore its potential for advancing quantitative medical research and enabling pervasive computing in healthcare. Our data and code are available at https://github.com/THU-CS-PI-LAB/ANFC-Automated-Nailfold-Capillary. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2312_05930 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis Zhao, Linxi Tang, Jiankai Chen, Dongyu Liu, Xiaohong Zhou, Yong Shi, Yuanchun Wang, Guangyu Wang, Yuntao Image and Video Processing Computer Vision and Pattern Recognition Machine Learning Nailfold capillaroscopy is widely used in assessing health conditions, highlighting the pressing need for an automated nailfold capillary analysis system. In this study, we present a pioneering effort in constructing a comprehensive nailfold capillary dataset-321 images, 219 videos from 68 subjects, with clinic reports and expert annotations-that serves as a crucial resource for training deep-learning models. Leveraging this dataset, we finetuned three deep learning models with expert annotations as supervised labels and integrated them into a novel end-to-end nailfold capillary analysis pipeline. This pipeline excels in automatically detecting and measuring a wide range of size factors, morphological features, and dynamic aspects of nailfold capillaries. We compared our outcomes with clinical reports. Experiment results showed that our automated pipeline achieves an average of sub-pixel level precision in measurements and 89.9% accuracy in identifying morphological abnormalities. These results underscore its potential for advancing quantitative medical research and enabling pervasive computing in healthcare. Our data and code are available at https://github.com/THU-CS-PI-LAB/ANFC-Automated-Nailfold-Capillary. |
| title | A Comprehensive Dataset and Automated Pipeline for Nailfold Capillary Analysis |
| topic | Image and Video Processing Computer Vision and Pattern Recognition Machine Learning |
| url | https://arxiv.org/abs/2312.05930 |