School of Electronics and Information, Hangzhou Dianzi University.
Department of Plastic and Reconstructive Surgery.
J Craniofac Surg. 2022;33(1):312-318. doi: 10.1097/SCS.0000000000008198.
This paper puts forward a new method for automatic segmentation of bony orbit as well as automatic extraction and classification of aging features of segmented orbit contour based on depth learning, with which the aging mode of bony orbit contour is preliminarily validated.
Three-dimensional reconstruction was carried out by using the craniofacial Computed Tomography scanning data of 595 adult Mongolians at different ages (119 young males, 78 young females, 109 middle-aged males, 89 middle-aged females, 95 elderly males, and 105 elderly females), the craniofacial images were exported, orbit contour images were obtained with U-Net segmentation network, and then the orbit contour features of young group, the middle-aged group and the elderly group were classified with the classification network. Next, contour area, height, and other features put forward in existing research were automatically calculated by using the connected component shape description method; and it was validated whether the aging features of the bony orbit only occur to partial or the whole orbit.
With the method put forward in this paper, high-precision identification (97.94% and 99.18%) of 3 categories in the male and female group experiments. In the meanwhile, it was found in the comparison experiment with other features that bony orbit contour definitely has features relating to aging, but these features only occur to partial areas of the orbit, which enables the convolutional neural network to achieve good identification effects. And, bone resorption of the superior orbital rim of males is more obvious than that of the inferior orbital rim, but the overall shape features like the bony orbit area and height do not change significantly along with the increase of the age.
U-Net can realize high-precision segmentation of the orbit contour, and with the Convolutional Neural Network-based orbit contour sorting algorithm, the aging degree of the bony orbit can be identified precisely. It is preliminarily validated that the aging mode of Mongolian bony orbit contour is that the bone resorption of the superior orbital rim is more obvious than that of the inferior orbital rim, and the change of the orbit area, perimeter, height and circularity is not obvious in the aging process.
本文提出了一种基于深度学习的骨性眼眶自动分割新方法，以及基于此方法的眼眶轮廓老化特征自动提取与分类，初步验证了骨性眼眶轮廓的老化模式。
对 595 例不同年龄（119 例青年男性、78 例青年女性、109 例中年男性、89 例中年女性、95 例老年男性、105 例老年女性）蒙古成年人颅颌面 CT 扫描数据进行三维重建，导出颅颌面图像，用 U-Net 分割网络得到眼眶轮廓图像，再用分类网络对青年组、中年组和老年组的眼眶轮廓特征进行分类。然后，用连通分量形状描述法自动计算现有研究中提出的轮廓面积、高度等特征；验证骨性眼眶的老化特征是否仅发生在眼眶的部分区域还是整个眼眶。
本文提出的方法在男性和女性组实验中，对 3 类的识别准确率达到了 97.94%和 99.18%。同时，在与其他特征的对比实验中发现，骨性眼眶轮廓确实存在与老化相关的特征，但这些特征仅发生在眼眶的部分区域，这使得卷积神经网络能够取得良好的识别效果。而且，男性的眶上缘骨吸收比眶下缘更明显，但随着年龄的增长，骨性眼眶的面积和高度等整体形态特征并没有明显变化。
U-Net 可以实现眼眶轮廓的高精度分割，通过基于卷积神经网络的眼眶轮廓分类算法，可以准确识别骨性眼眶的老化程度。初步验证了蒙古人骨性眼眶轮廓的老化模式是眶上缘骨吸收比眶下缘更明显，在老化过程中眼眶面积、周长、高度和圆度的变化不明显。
