Purpose: Current research on the aging of bony orbit is usually done manually, which is inefficient and has a large error. In this paper, automatic segmentation of bony orbit based on deep learning and automatic calculation of the parameters of the segmented orbital contour (area and height of bony orbit) are presented.
Methods: The craniofacial CT scanning data of 595 Chinese were used to carry out three-dimensional reconstruction and output the craniofacial images. The orbital contour images are obtained automatically by UNet++ segmentation network, and then the bony orbital area and height were calculated automatically by connected component analysis.
Results: The automatic segmentation method has an Intersection of Union of 95.41% in craniofacial CT images. During the aging, the bony orbital area of males increased with age, while that of females decreased, and the area in male was larger than that in female (P < 0.05). The distance from equal points 10 and 40-90 to the supraorbital rim was significantly larger (P < 0.05). Except for the equal point 90, the distance from equal points to the inferior orbital rim was obviously larger (P < 0.05). In the females, the distance from equal points 50-70 to inferior orbital rim was significantly lower (P < 0.05).
Conclusion: The method proposed here can automatically and accurately study image dataset of large-scale bony orbital CT imaging. UNet++ can achieve high-precision segmentation of bony orbital contours. The bony orbital area of Chinese changes with aging, and the bony orbital height changes different between males and females, which may be caused by the different position and degree of orbital bone resorption of males and females in the process of aging.
Keywords: Aging; Automatic; Bony orbital area; Bony orbital height; Connected component analysis; UNet++.
© 2022. The Author(s), under exclusive licence to Springer-Verlag France SAS, part of Springer Nature.