###### Abstract
The process is further complicated by several factors: (1) the small size of the glands, typically measuring 4-6 cm in length and weighing only 4-5 g in adults [17]; (2) variable shape and orientation depending on patient anatomy and respiration phase [18]; (3) limited contrast differentiation from surrounding retroperitoneal fat and adjacent vascular structures; and (4) proximity to organs with similar densities such as the pancreatic tail and splenic vessels [19]. These challenges make adrenal segmentation one of the most demanding tasks in abdominal image analysis, even for experienced radiologists. The field of medical image analysis has been revolutionized by recent advances in artificial intelligence (AI), particularly deep convolutional neural networks (CNNs), which have demonstrated remarkable capability in automated organ segmentation [20; 21]. Deep learning approaches have shown impressive results in segmenting larger abdominal organs such as the liver [22], kidneys [23], and spleen [24], with Dice similarity coefficients frequently exceeding 0.90. The U-Net architecture, first introduced by [PERSON] et al. [25], has emerged as a particularly effective framework for biomedical image segmentation due to its encoder-decoder structure with skip connections that preserve spatial information critical for precise boundary delineation. More recent architectural innovations have further improved medical image segmentation capability. Attention-based mechanisms have been integrated into CNN frameworks to enhance feature selection and boundary delineation [26]. Additionally, transformer-based architectures that leverage self-attention mechanisms have demonstrated promising results in capturing long-range dependencies in medical images [27; 28]. However, despite these advances, the literature on automated adrenal gland segmentation remains relatively sparse. [PERSON] et al. [29] reported preliminary results using a modified 3D U-Net for adrenal segmentation, achieving a Dice score of 0.74. Nonetheless, these studies typically employed single-phase CT images and relatively small datasets, limiting their generalizability to diverse clinical scenarios. The intricate anatomy and substantial variability of adrenal glands continue to present unique challenges for automated segmentation that have not been fully addressed by existing methodologies [30]. Alternative CNN architectures have shown promise in various medical segmentation tasks. SegNet, developed by [PERSON] et al. [31], utilizes an encoder-decoder structure with pooling indices to improve memory efficiency and has demonstrated efficacy in retinal image segmentation [32]. More recently, lightweight architectures such as NablaNet have emerged with an emphasis on computational efficiency while maintaining acceptable segmentation performance, particularly for edge computing applications in resource-constrained environments [33]. Multi-scale approaches have also shown promise in segmentation tasks involving structures with variable appearances. [PERSON] et al. [34] introduced a Multi-Scale Three-Path Network (MSTP-Net) for vessel segmentation that efficiently captures features at different resolutions, a concept potentially applicable to adrenal segmentation given the glands' thin, elongated portions and thicker body. This study proposes and systematically evaluates a deep learning-based approach for automatic adrenal gland segmentation using three distinct CNN architectures: U-Net, SegNet, and NablaNet. We specifically address the challenges of adrenal segmentation using multiphase CT imaging, which provides complementary tissue characterization information across different contrast phases. We assess performance through comprehensive metrics including segmentation quality (measured by the Dice similarity coefficient), boundary precision (Hausdorff distance), and practical implementation parameters such as time-to-model-integration-in-production (TTMIP), time-of-training-models (TOTM), and time-of-preprocessing-input (TOPPI).