書誌事項
- タイトル別名
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- Segmentation of Mandibular Canal on Dental Cone Beam CT Images with AI Development Support Software for Medical Images
- 公開日
- 2024
- 資源種別
- journal article
- DOI
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- 10.11242/dentalradiology.64.11
- 公開者
- 特定非営利活動法人 日本歯科放射線学会
この論文をさがす
説明
The primary barrier to the development of artificial intelligence (AI) in medical imaging is data collection. Medical professionals generate training data for the development of medical imaging AI; however, this process is time-consuming and labor-intensive. Consequently, insufficient data collection frequently results in the premature discontinuation of AI model development. To address this challenge, we developed a software tool named ‘Aidia’ to support the research and development of medical imaging AI. Aidia offers functionalities such as a medical image viewer, data annotation, model training for various tasks, model evaluation, and automated annotation using trained models. For data annotation, Aidia allows users to annotate images by generating polygons, rectangles, polylines, lines, and points. Aidia supports Digital Imaging and Communications in Medicine images as well as general image formats, and we optimized it for annotating medical images. Moreover, Aidia utilizes open-source Python and PyQt5 libraries to build a cross-platform graphical user interface. Thus, Aidia provides a platform where medical professionals can develop, evaluate, and create training data for AI models at no cost. In this study, we developed a segmentation model based on U-Net to predict mandibular canals in cross-sectional jawbone images using Aidia. We collected 8,287 images annotated by a radiologist and trained the segmentation model using these data. The trained model achieved a precision of 0.805, recall of 0.752, F1 score of 0.777, and average precision of 0.869 on test data, accurately generating training data for the test images. Aidia is a promising solution for AI development and image annotation in the medical field.
収録刊行物
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- 歯科放射線
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歯科放射線 64 (1), 11-19, 2024
特定非営利活動法人 日本歯科放射線学会
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詳細情報 詳細情報について
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- CRID
- 1390020209537387136
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- NII書誌ID
- AN00101479
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- ISSN
- 21856311
- 03899705
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- 本文言語コード
- ja
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- 資料種別
- journal article
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- データソース種別
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- JaLC
- IRDB
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- 抄録ライセンスフラグ
- 使用不可

