A Functionally Guided U-Net for Chronic Kidney Disease Assessment: Joint Structural Segmentation and eGFR Prediction with a Structure–Function Consistency Loss

An accurate assessment of chronic kidney disease (CKD) requires understanding both renal morphology and functional decline, yet most deep learning approaches treat segmentation and eGFR prediction as separate tasks. This paper proposes the Functionally Guided CKD U-Net (FG-CKD-UNet), a dual-headed multitask architecture that integrates multi-class kidney segmentation with end-to-end eGFR prediction using a structure–function consistency loss. The model incorporates a morphological biomarker extractor to derive cortical thickness, kidney volume, and cortex–medulla ratios, enabling explicit coupling between anatomy and physiology. Experiments on T2-weighted MRI and colorized CT datasets demonstrate that the proposed method surpasses state-of-the-art segmentation baselines, achieving a Dice score of 0.94 and an HD95 of 9.8 mm. For functional prediction, the model achieves an MAE of 0.039, an RMSE of 0.058, and a Pearson correlation of 0.92, outperforming CNN, MLP, and ResNet baselines. The structure–function consistency mechanism reduces the consistency error from 0.071 to 0.042, confirming coherent physiological modeling. The results indicate that the FG-CKD-UNet provides a reliable, interpretable, and physiologically grounded framework for comprehensive CKD assessment.