ReCo-KD: Region- and Context-Aware Knowledge Distillation for Efficient 3D Medical Image Segmentation
This work addresses the challenge of deploying accurate yet efficient 3D medical image segmentation models in resource-constrained clinical settings, where existing lightweight architectures often suffer significant performance degradation. To this end, we propose ReCo-KD, a training-stage knowledge distillation framework that effectively transfers fine-grained anatomical structures and long-range contextual information from a teacher network to a compact student model. Our approach leverages multi-scale structure-aware region distillation (MS-SARD) and multi-scale context alignment (MS-CA), incorporating class-aware masks, scale-normalized weighting, and cross-level feature affinity alignment. Notably, ReCo-KD operates independently of the student backbone and integrates seamlessly with nnU-Net. Extensive experiments demonstrate that our method substantially reduces model parameters and inference latency across multiple public and complex aggregated 3D medical datasets while preserving segmentation accuracy close to that of the teacher model, highlighting its strong potential for clinical deployment.