## 4 Discussion
[38] in their systematic review of deep learning approaches for medical image segmentation, highlighting the persistent challenge of optimizing both parameters simultaneously. The time-related key performance indicators revealed that while preprocessing (TOPPI) required considerable initial investment, particularly in YAML configuration development, it substantially facilitated subsequent training workflows across all architectures. Model deployment times (TTMIP) ranged from approximately 46 s to 72 s per adrenal gland segmentation during inference. These processing times, while longer than the sub-second inference reported by [PERSON] et al. [39] for larger abdominal organs, remain clinically acceptable for integration into radiological workflows given the specialized nature of adrenal assessment. When comparing our multiphase approach with single-phase methods, our results demonstrate modest improvements. This suggests potential benefits from incorporating complementary information across different contrast enhancement phases. However, [PERSON] et al. [40] achieved comparable adrenal segmentation accuracy using only single-phase images by implementing an advanced boundary refinement module, suggesting alternative architectural innovations may potentially compensate for limited phase information. In the broader context of small retroperitoneal structure segmentation, our adrenal gland results (Dice: 0.63-0.66) compare favorably with reported outcomes for similarly challenging anatomical targets such as pancreatic tail (Dice: 0.54-0.67) in the work of [PERSON] et al. [41]. This suggests that the performance limitations may reflect inherent challenges in small structure segmentation rather than specific deficiencies in our methodological approach. The clinical utility of automated adrenal segmentation extends beyond simple delineation. Accurate volumetric assessment of adrenal glands could potentially enhance diagnostic algorithms for conditions such as adrenal hyperplasia and subclinical Cushing's syndrome. Similarly, [PERSON] et al. [42] established correlations between quantitative CT features and adrenal functional status, suggesting potential applications for AI-derived imaging biomarkers. Recent advances in deep learning methodology offer promising directions for improving adrenal segmentation performance. Attention gate mechanisms, as implemented by [PERSON] et al. [43], have demonstrated particular efficacy for small structure segmentation by adaptively highlighting relevant features while suppressing irrelevant regions. Similarly, transformer-based architectures like TransUNet proposed by [PERSON] et al. [44] leverage self-attention mechanisms to capture long-range dependencies, potentially addressing the contextual challenges inherent in adrenal segmentation. The integration of these advanced architectural elements represents a logical next step for improving performance beyond our baseline implementations. This study has several limitations that warrant acknowledgment. First, our dataset size, while substantial for adrenal segmentation research, remains relatively small comparedto large public databases used for other organ segmentation tasks. [PERSON] et al. [45] demonstrated that segmentation performance for challenging structures can continue to improve with dataset sizes exceeding 1000 cases, suggesting potential benefits from further data collection. Second, the use of multiphase imaging from different CT manufacturers introduces potential variability that could affect model generalizability. Third, external validation on truly independent datasets was not performed, limiting assessment of generalizability across different clinical settings and acquisition protocols. A limitation of our current study is the absence of external validation on independent datasets from different institutions and scanner types.