Disagreement-Guided Knowledge Distillation for Efficient Kidney Segmentation in Abdominal CT

Accurate kidney segmentation in abdominal computed tomography (CT) images is important for quantitative analysis and computer-assisted clinical workflows, yet deploying deep learning models in practice remains challenging due to computational constraints. To address this, a disagreement-guided knowledge distillation (KD) framework is proposed for efficient kidney segmentation. The method introduces a spatial disagreement mask (Ω) to identify regions where teacher and student predictions diverge, enabling selective knowledge transfer focused on informative and error-prone areas while avoiding redundant supervision in well-predicted regions. In addition, a pixel-level annotated kidney segmentation dataset is created by extending a previously published abdominal CT dataset with kidney masks. The experimental results on both the in-house dataset and the KiTS19 benchmark show improved overlap-based segmentation performance, particularly in Dice and IoU, compared with supervised training and conventional pixel-wise KD. On the in-house dataset, Dice increases from 0.9239 to 0.9335 and IoU from 0.8720 to 0.8859, together with improved boundary-based distance metrics. On KiTS19, Dice improves from 0.8732 to 0.8812, primarily driven by improved kidney recall and a reduction in under-segmentation errors; however, boundary-based distance metrics remain more favorable for the conventional pixel-wise KD under domain shift. Additional experiments with a compact Attention U-Net-small student and stronger teacher sources further show that KD-Ω can improve compact student performance, although the magnitude of improvement depends on the teacher prediction profile. These findings indicate that the proposed framework provides an efficient and practical approach for enhancing lightweight segmentation models by prioritizing clinically relevant foreground preservation and reducing missed kidney regions under computational constraints.