A Survey on lightweight technology of neural networks for medical image segmentation

Recent advances in medical image segmentation have significantly improved segmentation accuracy. Nevertheless, the clinical deployment of large-scale segmentation networks remains constrained by challenges such as excessive parameter counts, complex architectures, and limited adaptability to diverse deployment environments. The absence of lightweight design further restricts their integration into resource-limited edge devices. To address these barriers, lightweight strategies have emerged as an effective solution. Structural optimization simplifies network architectures to reduce computational costs, while model compression techniques shrink model size without sacrificing performance. At the same time, hardware-level acceleration provides additional support for efficient inference in real-world scenarios. This review systematically summarizes recent lightweight methods for medical image segmentation from both software and hardware perspectives. Representative algorithmic approaches are highlighted, including pruning, quantization, knowledge distillation, and efficient network architectures, along with hardware-aware optimization strategies tailored for edge deployment. Moreover, we explored the mainstream approach of integrating large-scale models with lightweight technologies to achieve the optimal balance between segmentation accuracy and computational efficiency. Finally, current limitations and potential research directions are outlined to promote the translation of lightweight segmentation models into routine clinical workflows. By providing a structured reference, this review aims to support researchers and practitioners in advancing the efficient and practical application of medical image segmentation in clinical environments.