Age-related morphometrics of normal adrenal glands based on deep learning-aided segmentation

Abstract

Objective.This study aims to evaluate the morphometrics of normal adrenal glands in adult patients semiautomatically using a deep learning-based segmentation model.Materials and Methods.A total of 520 abdominal CT image series with normal findings, from January 1, 2016, to March 14, 2019, were retrospectively collected for the training of the adrenal segmentation model. Then, 1043 portal venous phase image series of inpatient contrast-enhanced abdominal CT examinations with normal adrenal glands were included for analysis and grouped by every 10-year gap. A 3D U-Net-based segmentation model was used to predict bilateral adrenal labels followed by manual modification of labels as appropriate. Quantitative parameters (volume, CT value, and diameters) of the bilateral adrenal glands were then analyzed.Results.In the study cohort aged 18–77 years old (554 males and 489 females), the left adrenal gland was significantly larger than the right adrenal gland [all patients, 2867.79 (2317.11–3499.89) mm3 vs. 2452.84 (1983.50–2935.18) mm3, P < 0.001]. Male patients showed a greater volume of bilateral adrenal glands than females in all age groups (all patients, left: 3237.83 ± 930.21 mm3 vs. 2646.49 ± 766.42 mm3, P < 0.001; right: 2731.69 ± 789.19 mm3 vs. 2266.18 ± 632.97 mm3, P = 0.001). Bilateral adrenal volume in male patients showed an increasing then decreasing trend as age increased that peaked at 38–47 years old (left: 3416.01 ± 886.21 mm3, right: 2855.04 ± 774.57 mm3).Conclusions.The semiautomated measurement revealed that the adrenal volume differs as age increases. Male patients aged 38–47 years old have a peaked adrenal volume.

Keywords

• Adrenal gland
• Deep learning
• Morphometrics
• Computed tomography