Objective: 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. Method: 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. Results: 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. Conclusions: 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.

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Background Postoperative restenosis frequently occurs in intracranial atherosclerotic disease (ICAD) patients after drug‐coated balloon (DCB) treatment. However, high‐risk plaques associated with postoperative restenosis remain to be explored. Purpose To assess whether high‐resolution vessel wall MRI (HR‐VWI) contributes to the identification of high‐risk plaques associated with postoperative restenosis before DCB treatment. Study Type Retrospective. Subjects A total of 70 patients with ICAD who underwent DCB treatment. Field Strength/Sequence 3.0 T; magnetic resonance angiography, HR‐VWI. Assessment All patients underwent HR‐VWI examination prior to DCB treatment. Digital subtraction angiography (DSA) measurement was assessed 6 months (±1 month) after operation to determine the vessel restenosis, classifying patients into three groups of no stenosis, mild stenosis (<50%), and restenosis (>50%). Clinical factors and HR‐VWI characteristics, including vessel and lumen area at maximal lumen narrowing (MLN), plaque area and length, degree of stenosis, plaque burden, remodeling index, and enhancement amplitude, were compared among three groups. Clinical factors and HR‐VWI characteristics were separately evaluated for the association with postoperative restenosis. Statistical Tests Kolmogorov–Smirnov test, intra‐class correlation coefficient, Kruskal Wallis H test, Mann–Whitney U test, receiver operating characteristic (ROC) curve, multivariable linear regression analysis. P‐values <0.05 was considered statistically significant. Results During the follow‐up DSA measurement, 13 lesions (18.5%) showed restenosis. With HR‐VWI, significant differences among three groups were observed in plaque length, lumen area of MLN, degree of stenosis, enhancement amplitude, and plaque burden. In ROC analysis, plaque length (area under the curve [AUC] = 0.809), and enhancement amplitude (AUC = 0.880) provided higher efficacy in identification of high‐risk plaques associated with postoperative restenosis than degree of stenosis (AUC = 0.746) and plaque burden (AUC = 0.759). Multivariable linear regression analysis showed that plaque length and enhancement amplitude were independent prognostic factors of postoperative restenosis. Data Conclusion HR‐VWI has the potential to identify high‐risk plaques in ICAD patients before DCB treatment. Level of Evidence 4 Technical Efficacy Stage 2