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

The useful part

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.

How it works

• 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.
• The model was rigorously evaluated on a private clinical dataset (n=218) and the KiTS19 benchmark (n=210).
• on the kits19 dataset maintained robust dsc to facilitate clinical translation demonstration-only online platform was developed.
• conclusions: gam-deeplabv3 framework provides an accurate efficient fully automated solution for renal tumor segmentation.
• by overcoming boundary ambiguity optimizing feature fusion this approach shows potential as decision-support aid pending f...

What to take from it

There is an urgent clinical demand for robust, automated segmentation solutions. We propose an optimized encoder-decoder architecture specifically tailored for renal mass detection. Results: GAM-DeepLabV3+ consistently outperformed state-of-the-art baselines.