TY - JOUR
T1 - Quantifying gender differences in orbital morphology with large MRI datasets
AU - Han, Yingxiang
AU - Li, Qi
AU - Liu, Tingting
AU - Chen, Zengsheng
AU - Chen, Xi
AU - Wang, Xiaofei
N1 - Publisher Copyright:
© 2024
PY - 2024/12
Y1 - 2024/12
N2 - Purpose: To investigate gender differences in orbital morphology using large MRI datasets. Methods: Using a deep learning-based approach, the orbit and eyeball were automatically segmented from high resolution 3D MRI images of the IXI and OASIS3 datasets. Orbital and eyeball morphological parameters, including orbital volume, eyeball volume, effective orbital volume (EOV, defined as the orbital cavity volume excluding the eyeball), and coronal orbital dimensions and shape, were quantitatively assessed. The volume index was defined as the ratio of orbital volume to eyeball volume. Results: This study included 1926 subjects with a mean age of 63.9 ± 15.3 years. The mean volumes of the eyeball and orbit were 7.1 ± 1.0 ml and 25.9 ± 3.5 ml, respectively. Significant gender differences (all P < 0.001) were observed in the following parameters (males versus females): orbital volume (28.3 ± 3.0 ml versus 24.0 ± 2.7 ml), EOV (25.1 ± 3.0 ml versus 21.1 ± 2.6 ml), eyeball volume (7.3 ± 1.0 ml versus 6.9 ± 1.0 ml), volume index (3.9 ± 0.6 versus 3.5 ± 0.5), orbital depth (40.0 ± 3.1 mm versus 37.4 ± 2.9 mm), coronal orbital height (40.8 ± 3.0 mm versus 38.4 ± 2.4 mm), coronal orbital width (38.0 ± 1.9 mm versus 36.6 ± 1.7 mm) and coronal orbital area (1292.5 ± 97.1 mm2 versus 1177.9 ± 89.7 mm2). Conclusions: This study employed deep learning to analyze a large dataset of 3D head MRI scans, achieving accurate and objective measurements of orbital morphology. We identified significant gender differences in orbital parameters, with males generally having larger structures. Additionally, we established a normative database for orbital dimensions, providing a valuable resource for future research on orbital disorders and potentially improving clinical diagnosis and treatment.
AB - Purpose: To investigate gender differences in orbital morphology using large MRI datasets. Methods: Using a deep learning-based approach, the orbit and eyeball were automatically segmented from high resolution 3D MRI images of the IXI and OASIS3 datasets. Orbital and eyeball morphological parameters, including orbital volume, eyeball volume, effective orbital volume (EOV, defined as the orbital cavity volume excluding the eyeball), and coronal orbital dimensions and shape, were quantitatively assessed. The volume index was defined as the ratio of orbital volume to eyeball volume. Results: This study included 1926 subjects with a mean age of 63.9 ± 15.3 years. The mean volumes of the eyeball and orbit were 7.1 ± 1.0 ml and 25.9 ± 3.5 ml, respectively. Significant gender differences (all P < 0.001) were observed in the following parameters (males versus females): orbital volume (28.3 ± 3.0 ml versus 24.0 ± 2.7 ml), EOV (25.1 ± 3.0 ml versus 21.1 ± 2.6 ml), eyeball volume (7.3 ± 1.0 ml versus 6.9 ± 1.0 ml), volume index (3.9 ± 0.6 versus 3.5 ± 0.5), orbital depth (40.0 ± 3.1 mm versus 37.4 ± 2.9 mm), coronal orbital height (40.8 ± 3.0 mm versus 38.4 ± 2.4 mm), coronal orbital width (38.0 ± 1.9 mm versus 36.6 ± 1.7 mm) and coronal orbital area (1292.5 ± 97.1 mm2 versus 1177.9 ± 89.7 mm2). Conclusions: This study employed deep learning to analyze a large dataset of 3D head MRI scans, achieving accurate and objective measurements of orbital morphology. We identified significant gender differences in orbital parameters, with males generally having larger structures. Additionally, we established a normative database for orbital dimensions, providing a valuable resource for future research on orbital disorders and potentially improving clinical diagnosis and treatment.
KW - Deep learning
KW - Eyeball
KW - Gender
KW - MRI
KW - Orbit
UR - https://www.scopus.com/pages/publications/85203560414
U2 - 10.1016/j.medntd.2024.100332
DO - 10.1016/j.medntd.2024.100332
M3 - 文章
AN - SCOPUS:85203560414
SN - 2590-0935
VL - 24
JO - Medicine in Novel Technology and Devices
JF - Medicine in Novel Technology and Devices
M1 - 100332
ER -