Automated Renal Tumor Segmentation in Computed Tomography Images Using a Global Attention-Based DeepLabV3+ Model: Algorithm Development and Validation.

Background: The rising global incidence of renal tumors necessitates precise diagnostic interventions. Accurate segmentation of computed tomography (CT) scans is essential for nephron-sparing surgery and radiotherapy. However, conventional manual delineation is labor-intensive and prone to significant interobserver variability due to tumor morphological heterogeneity. There is an urgent clinical demand for robust, automated segmentation solutions. Objective: This study aims to develop and validate GAM-DeepLabV3+, an automated framework designed to address boundary ambiguity and high false-positive rates in complex renal imaging scenarios. Methods: We propose an optimized encoder-decoder architecture specifically tailored for renal mass detection. The framework incorporates three key innovations: (1) a lightweight MobileNetV2 backbone to minimize computational overhead for clinical deployment; (2) an Atrous Spatial Pyramid Pooling (ASPP) module to capture multiscale contextual information; and (3) a Global Attention Mechanism (GAM) in the decoder to enhance channel-spatial interactions, thereby refining boundary delineation by suppressing background noise. The model was rigorously evaluated on a private clinical dataset (n=218) and the KiTS19 benchmark (n=210). Results: <.001). It also achieved a mean 95% Hausdorff distance (HD95) of 1.485 (SD 0.522) pixels. On the KiTS19 dataset, it maintained a mean robust DSC of 0.928 (SD 0.006). To facilitate clinical translation, a demonstration-only online platform was developed. Conclusions: The GAM-DeepLabV3+ framework provides an accurate, efficient, and fully automated solution for renal tumor segmentation. By overcoming boundary ambiguity and optimizing feature fusion, this approach shows potential as a decision-support aid, pending future validation with 3D reconstruction.