###### Abstract  
Adrenal glands are vital endocrine organs whose accurate segmentation on CT imaging presents significant challenges due to their small size and variable morphology. This study evaluates the efficacy of deep learning approaches for automatic adrenal gland segmentation from multiphase CT scans. We implemented three convolutional neural network architectures (U-Net, SegNet, and NablaNet) and assessed their performance on a dataset comprising 868 adrenal glands from contrast-enhanced abdominal CT scans. Performance was evaluated using the Dice similarity coefficient (DSC), alongside practical implementation metrics including training and deployment time. U-Net demonstrated superior segmentation performance (DSC: 0.630 \(\pm\) 0.05 for right, 0.660 \(\pm\) 0.06 for left adrenal glands) compared to NablaNet (DSC: 0.552 \(\pm\) 0.08 for right, 0.550 \(\pm\) 0.07 for left) and SegNet (DSC: 0.320 \(\pm\) 0.10 for right, 0.335 \(\pm\) 0.09 for left). While all models achieved high specificity, boundary delineation accuracy remained challenging. Our findings demonstrate the feasibility of deep learning-based adrenal gland segmentation while highlighting the persistent challenges in achieving the segmentation quality observed with larger abdominal organs. U-Net provides the optimal balance between accuracy and computational requirements, establishing a foundation for further refinement of AI-assisted adrenal imaging tools. deep learning; adrenal gland; CNN models +
Footnote †: journal: Applied sciences  
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0000-comprises a cortex and medulla with distinct embryological origins, histological features, and endocrine functions [3]. The right adrenal gland typically exhibits a pyramidal configuration and is situated between the diaphragm posteriorly and the inferior vena cava anteromerdially, while the left adrenal gland presents a more crescentic morphology and is located between the diaphragm, stomach, pancreas, and left kidney [4; 5]. Recent advancements in imaging techniques have further enhanced our understanding of adrenal anatomy and pathophysiology. State-of-the-art multidetector CT and dual-energy CT provide superior spatial resolution and improved tissue characterization, while functional imaging modalities offer complementary information about adrenal activity [6; 7]. Pathological conditions affecting the adrenal glands encompass a wide spectrum of disorders, including adenomas, pheochromocytomas, adrenocortical carcinoma, metastases, hyperplasia, and inflammatory processes [8]. The prevalence of adrenal incidentalomas--adrenal masses discovered incidentally during imaging for unrelated conditions--has increased significantly with the widespread use of cross-sectional imaging, with studies reporting incidence rates of 1-5% in abdominal CT examinations [9; 10]. Comprehensive evaluation of these incidentalomas necessitates accurate morphological assessment, including precise measurement of size, density, enhancement patterns, and growth dynamics over time [11]. Computed tomography (CT) serves as the cornerstone of adrenal imaging due to its excellent spatial resolution, widespread availability, and ability to characterize lesion density and enhancement characteristics [10]. Multiphase CT protocols, encompassing unenhanced, arterial, venous, and delayed phases, allow for comprehensive evaluation of adrenal masses [12] through assessment of pre-contrast density, enhancement patterns, and washout characteristics [13; 14]. However, accurate interpretation of adrenal imaging studies presents significant challenges, particularly in the context of small lesions, complex regional anatomy, and variability in normal adrenal morphology [15]. Manual segmentation of adrenal glands on CT images, while facilitating precise volumetric and morphometric analysis, is exceptionally time-consuming and labor-intensive [16].