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| http://dx.doi.org/10.1186/s12880-024-01410-5 | DOI Listing |
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Correction: YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.
BMC Med Imaging
August 2024
Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey.
YOLO-V5 based deep learning approach for tooth detection and segmentation on pediatric panoramic radiographs in mixed dentition.
BMC Med Imaging
July 2024
Department of Oral and Maxillofacial Radiology, Faculty of Dentistry, Eskisehir Osmangazi University, Eskişehir, Turkey.
Objectives: In the interpretation of panoramic radiographs (PRs), the identification and numbering of teeth is an important part of the correct diagnosis. This study evaluates the effectiveness of YOLO-v5 in the automatic detection, segmentation, and numbering of deciduous and permanent teeth in mixed dentition pediatric patients based on PRs.
Methods: A total of 3854 mixed pediatric patients PRs were labelled for deciduous and permanent teeth using the CranioCatch labeling program.
Image recognition-based petal arrangement estimation.
Front Plant Sci
April 2024
Graduate School of Informatics, Osaka Metropolitan University, Sakai, Japan.
Flowers exhibit morphological diversity in the number and positional arrangement of their floral organs, such as petals. The petal arrangements of blooming flowers are represented by the overlap position relation between neighboring petals, an indicator of the floral developmental process; however, only specialists are capable of the petal arrangement identification. Therefore, we propose a method to support the estimation of the arrangement of the perianth organs, including petals and tepals, using image recognition techniques.
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