ArticleInternational neurourology journal2026
Impact of Simulated Radiation Dose Reduction on Deep Learning-Based Renal Segmentation Performance: A Simulation Study Using the KiTS21 (2021 Kidney and Kidney Tumor Segmentation Challenge) Dataset.
Article in International neurourology journal, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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- Evolutionary Frontiers in Urology: Expanding Clinical Care Through Devices and Intelligence.International neurourology journal · 2026Article
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Abstract
purposeThis study aimed to quantitatively evaluate the effect of simulated radiation dose reduction on deep learning-based renal segmentation performance and to identify a clinically acceptable minimum dose threshold.
methodsUsing the KiTS21 (2021 Kidney and Kidney Tumor Segmentation Challenge) dataset, which included 299 contrastenhanced computed tomography volumes with expert segmentation labels, 4 dose levels were simulated: 100%, 50%, 25%, and 10%. Dose reduction was simulated using Poisson noise modeling. A 2-dimensional U-Net with a ResNet34 encoder was trained exclusively on standard-dose images and evaluated across all dose levels using 5-fold cross-validation. Case-level performance was assessed using the Dice similarity coefficient (DSC), intersection over union, 95th-percentile Hausdorff distance (HD95), and volumetric error. Statistical significance was evaluated using the Wilcoxon signed-rank test with effect-size analysis.
resultsAt the standard dose, the model achieved a case-level DSC of 0.948±0.044. Performance remained stable at 50% dose (0.945±0.046), declined moderately at 25% dose (0.939±0.052), and decreased more substantially at 10% dose (0.921±0.069). The Wilcoxon signed-rank test showed statistically significant differences between 100% dose and all reduced dose levels (P<0.001). HD95 increased from 4.73±4.82 pixels at 100% dose to 6.58±6.74 pixels at 10% dose.
conclusionDeep learning-based renal segmentation demonstrated substantial robustness to simulated dose reduction. Performance remained clinically acceptable, with a DSC>0.93, down to 25% of the standard dose, suggesting that substantial dose reduction may be feasible without critically compromising artificial intelligence-assisted renal segmentation. The marked performance decline at 10% dose identifies a potential lower bound for clinical dose optimization.
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