## 4 Discussion  
This study explored the feasibility and effectiveness of automatic adrenal gland segmentation from multiphase CT scans using three distinct deep convolutional neural network architectures: U-Net, NablaNet, and SegNet. Among these, U-Net demonstrated the strongest overall performance, achieving Dice similarity coefficients of 0.630 and 0.660 for the right and left adrenal glands, respectively. While promising, these results remain notably lower than segmentation performance typically reported for larger and more distinct abdominal organs such as kidneys or liver, where Dice scores frequently exceed 0.85 [1; 2]. The moderate segmentation accuracy observed can be attributed to several anatomical and technical factors. First, adrenal glands present inherent segmentation challenges due to their small size, irregular morphology, and close proximity to other high-density anatomical structures, including the inferior vena cava, aorta, and diaphragmatic crura. These adjacent structures often exhibit CT attenuation values similar to those of adrenal tissue, complicating accurate boundary delineation even for experienced radiologists. Second, the partial volume effect inherent to CT imaging further exacerbates boundary ambiguity, particularly in thinner portions of the glands. Modern deep learning approaches have attempted to address these limitations through various architectural innovations. Multi-scale feature extraction, as demonstrated by [PERSON] et al. [34] in vascular segmentation tasks, could potentially enhance the delineation of thin adrenal limbs while preserving the overall glandular structure. Similarly, data fusion techniques, as reviewed by [PERSON] et al. [35], might facilitate the integration of complementary information from different imaging phases, potentially improving segmentation accuracy. Our findings align with the work of [PERSON] et al. [29], who reported Dice scores of 0.74 for adrenal segmentation using a modified 3D U-Net, slightly outperforming our results. This performance difference may be attributed to their utilization of a larger training dataset (over 1200 cases) and the implementation of additional postprocessing steps. The superior performance of U-Net aligns with the existing literature highlighting the effectiveness of encoder-decoder architectures with skip connections for small object segmentation [25]. These skip connections preserve spatial information throughout the network, allowing for more precise localization of subtle anatomical boundaries. A comprehensive comparative study by [PERSON] et al. [36] demonstrated U-Net's consistent superiority across multiple organ segmentation tasks, particularly for structures with complex morphological characteristics. In contrast, SegNet, primarily designed for larger object segmentation with an emphasis on computational efficiency, demonstrated significantly poorer performance in both overall accuracy (Dice score) and boundary precision (HD95) in our study, consistent with findings by [PERSON] et al. [31] regarding its limitations for small structure delineation. Notably, our observed laterality difference--with left adrenal glands consistently demonstrating better segmentation performance across all architectures--diverges from findings by [PERSON] et al. [37], who reported comparable performance bilaterally in their deep learning-based adrenal segmentation study. This discrepancy may reflect anatomical considerations, as the right adrenal gland's proximity to the liver and inferior vena cava presents unique challenges for segmentation algorithms. Although NablaNet offered faster training and inference times due to its lightweight architecture, this computational efficiency came at the cost of reduced segmentation quality. This observation suggests a clear trade-off between processing speed and segmentation accuracy that requires careful consideration when developing systems for clinical deployment, where diagnostic reliability remains paramount. Similar efficiency-accuracy trade-offs have been documented by [PERSON] et al.