UltraEar: a multicentric, large-scale database combining ultra-high-resolution computed tomography and clinical data for ear diseases
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arXiv
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| Auteurs principaux: | , , , , , , , , , , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866909756963160064 |
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| author | Tang, Ruowei Zhao, Pengfei Li, Xiaoguang Xu, Ning Cheng, Yue Zhang, Mengshi Wang, Zhixiang Zhang, Zhengyu Yin, Hongxia Ding, Heyu Gong, Shusheng Liu, Yuhe Wang, Zhenchang |
| author_facet | Tang, Ruowei Zhao, Pengfei Li, Xiaoguang Xu, Ning Cheng, Yue Zhang, Mengshi Wang, Zhixiang Zhang, Zhengyu Yin, Hongxia Ding, Heyu Gong, Shusheng Liu, Yuhe Wang, Zhenchang |
| contents | Ear diseases affect billions of people worldwide, leading to substantial health and socioeconomic burdens. Computed tomography (CT) plays a pivotal role in accurate diagnosis, treatment planning, and outcome evaluation. The objective of this study is to present the establishment and design of UltraEar Database, a large-scale, multicentric repository of isotropic 0.1 mm ultra-high-resolution CT (U-HRCT) images and associated clinical data dedicated to ear diseases. UltraEar recruits patients from 11 tertiary hospitals between October 2020 and October 2035, integrating U-HRCT images, structured CT reports, and comprehensive clinical information, including demographics, audiometric profiles, surgical records, and pathological findings. A broad spectrum of otologic disorders is covered, such as otitis media, cholesteatoma, ossicular chain malformation, temporal bone fracture, inner ear malformation, cochlear aperture stenosis, enlarged vestibular aqueduct, and sigmoid sinus bony deficiency. Standardized preprocessing pipelines have been developed for geometric calibration, image annotation, and multi-structure segmentation. All personal identifiers in DICOM headers and metadata are removed or anonymized to ensure compliance with data privacy regulation. Data collection and curation are coordinated through monthly expert panel meetings, with secure storage on an offline cloud system. UltraEar provides an unprecedented ultra-high-resolution reference atlas with both technical fidelity and clinical relevance. This resource has significant potential to advance radiological research, enable development and validation of AI algorithms, serve as an educational tool for training in otologic imaging, and support multi-institutional collaborative studies. UltraEar will be continuously updated and expanded, ensuring long-term accessibility and usability for the global otologic research community. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_20141 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | UltraEar: a multicentric, large-scale database combining ultra-high-resolution computed tomography and clinical data for ear diseases Tang, Ruowei Zhao, Pengfei Li, Xiaoguang Xu, Ning Cheng, Yue Zhang, Mengshi Wang, Zhixiang Zhang, Zhengyu Yin, Hongxia Ding, Heyu Gong, Shusheng Liu, Yuhe Wang, Zhenchang Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition Ear diseases affect billions of people worldwide, leading to substantial health and socioeconomic burdens. Computed tomography (CT) plays a pivotal role in accurate diagnosis, treatment planning, and outcome evaluation. The objective of this study is to present the establishment and design of UltraEar Database, a large-scale, multicentric repository of isotropic 0.1 mm ultra-high-resolution CT (U-HRCT) images and associated clinical data dedicated to ear diseases. UltraEar recruits patients from 11 tertiary hospitals between October 2020 and October 2035, integrating U-HRCT images, structured CT reports, and comprehensive clinical information, including demographics, audiometric profiles, surgical records, and pathological findings. A broad spectrum of otologic disorders is covered, such as otitis media, cholesteatoma, ossicular chain malformation, temporal bone fracture, inner ear malformation, cochlear aperture stenosis, enlarged vestibular aqueduct, and sigmoid sinus bony deficiency. Standardized preprocessing pipelines have been developed for geometric calibration, image annotation, and multi-structure segmentation. All personal identifiers in DICOM headers and metadata are removed or anonymized to ensure compliance with data privacy regulation. Data collection and curation are coordinated through monthly expert panel meetings, with secure storage on an offline cloud system. UltraEar provides an unprecedented ultra-high-resolution reference atlas with both technical fidelity and clinical relevance. This resource has significant potential to advance radiological research, enable development and validation of AI algorithms, serve as an educational tool for training in otologic imaging, and support multi-institutional collaborative studies. UltraEar will be continuously updated and expanded, ensuring long-term accessibility and usability for the global otologic research community. |
| title | UltraEar: a multicentric, large-scale database combining ultra-high-resolution computed tomography and clinical data for ear diseases |
| topic | Image and Video Processing Artificial Intelligence Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2508.20141 |