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. 2025 May 1;25(1):142.
doi: 10.1186/s12880-025-01682-5.

A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images

Fanxing Meng #  1 , Tuo Zhang #  1 , Yukun Pan  1 , Xiaojing Kan  1 , Yuwei Xia  2 , Mengyuan Xu  1 , Jin Cai  1 , Fangbin Liu  1 , Yinghui Ge  3
Affiliations

A deep learning algorithm for automated adrenal gland segmentation on non-contrast CT images

Fanxing Meng et al. BMC Med Imaging. .

Abstract

Background: The adrenal glands are small retroperitoneal organs, few reference standards exist for adrenal CT measurements in clinical practice. This study aims to develop a deep learning (DL) model for automated adrenal gland segmentation on non-contrast CT images, and to conduct a preliminary large-scale study on age-related volume changes in normal adrenal glands using the model output values.

Methods: The model was trained and evaluated on a development dataset of annotated non-contrast CT scans of bilateral adrenal glands, utilizing nnU-Net for segmentation task. The ground truth was manually established by two experienced radiologists, and the model performance was assessed using the Dice similarity coefficient (DSC). Additionally, five radiologists provided annotations on a subset of 20 randomly selected cases to measure inter-observer variability. Following validation, the model was applied to a large-scale normal adrenal glands dataset to segment adrenal glands.

Results: The DL model development dataset contained 1301 CT examinations. In the test set, the median DSC scores for the segmentation model of left and right adrenal glands were 0.899 and 0.904 respectively, and in the independent test set were 0.900 and 0.896. Inter-observer DSC for radiologist manual segmentation did not differ from automated machine segmentation (P = 0.541). The large-scale normal adrenal glands dataset contained 2000 CT examinations, the graph shows that adrenal gland volume increases first and then decreases with age.

Conclusion: The developed DL model demonstrates accurate adrenal gland segmentation, and enables a comprehensive study of age-related adrenal gland volume variations.

Keywords: Adrenal gland; Adrenal gland segmentation; Dice similarity coefficient; Non-contrast CT; nnU-Net.

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Conflict of interest statement

Declarations. Ethics approval and consent to participate: This study was approved by the Ethics Committee of Fuwai Central-China Cardiovascular Hospital (Approval No. 2025.21). Given the retrospective nature of the study and the use of fully anonymized patient data, the requirement for informed consent was waived by the ethics committee. All procedures were conducted in accordance with the ethical standards outlined in the Declaration of Helsinki and its later amendments. Consent for publication: Not applicable. Competing interests: The authors declare no competing interests.

Figures

Fig. 1
Fig. 1
Flowchart of Data Collection and Grouping for (a) Adrenal Gland Segmentation Development Dataset from 1432 Non-contrast CT Imagings (2018–2020) and (b) Large-Scale Normal Adrenal Glands Dataset of 2000 Subjects for Age-Related Volume Analysis
Fig. 2
Fig. 2
Workflow of Adrenal Gland Segmentation Using nnU-Net on Non-contrast CT Images. This diagram shows adrenal gland segmentation using nnU-Net on non-contrast CT images. Inputs are 2D/3D CT images with ROI/VOI annotations (2D/3D masks). Preprocessing includes cropping, resampling, normalization, and data augmentation. Data are processed by 2D/3D nnU-Net with 5-fold cross-validation, followed by external testing. Post-processing refines segmentation and calculates volume
Fig. 3
Fig. 3
Comparison of Left Adrenal Gland Segmentation by Five Radiologists and nnU-Net in One Patient. Radiologists annotated the gland on a non-contrast CT image. Median inter-observer DSC was 0.781–0.973, and model-observer DSC was 0.791–0.909 (P = 0.541), showing high agreement
Fig. 4
Fig. 4
Age-Related Changes in Bilateral Adrenal Gland Volume in Normal Subjects by Sex (Male: n = 1328; Female: n = 672). This graph shows age-related changes in bilateral adrenal gland volume in 2000 normal subjects, segmented by nnU-Net. Subjects were grouped by 20-year age intervals, with males (n = 1328, upper curve) and females (n = 672, lower curve) analyzed separately. Volume increases then decreases with age, with males having larger volumes
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