Abstract
<jats:p>Introduction: Automatic segmentation of target volumes and organs at risk may save time and reduce inter-observer variability. However, commercial systems are costly, difficult to customise, and often poorly represent local populations. We developed a deep learning-based automatic segmentation system (DRAW), based on a client-server architecture using a decoupled inference layer to enable on-demand use of computational resources. We report the initial deployment experience and performance across two centres.Materials and Methods: The DRAW system was deployed at two geographically distant centres. System uptime, successful segmentation rate, and segmentation time were recorded. Spatial overlap metrics, including the Dice similarity coefficient (DSC), Dice Jaccard coefficient (DJC), 95th percentile Hausdorff distance (HD95), surface Dice similarity coefficient (sDSC), and mean distance to conformity (MDC), were computed for a sampled subset where automatic segmentation and manually modified structures were available. A qualitative assessment of the extent of contour modification required was performed independently at one centre. Inter-centre differences were assessed using independent-samples t-tests.Results: Between April 2025 and April 2026, 4215 of 4303 uploaded series (98%) were successfully segmented. The API server uptime exceeded 99.5%. Median segmentation time was 20.2 minutes. Quantitative evaluation of segmented structures was performed in 380 patients. Aggregated mean DSC was 0.81 at Centre A and 0.78 at Centre B. Other spatial overlap metrics also showed a lower performance in Centre B. On qualitative review at Centre B, no modification was required for 24% of structures, while major and minor modifications were required for 33% and 31% of structures, respectively.Conclusion: The DRAW system was deployed and maintained across two centres with acceptable performance. Differences in model performance between the training and external centre indicate the need for more robust training. Open-source tools can reduce barriers to adopting automatic segmentation in resource-constrained settings.</jats:p>