Abstract
Medical image segmentation requires both high accuracy and computational efficiency, especially in resource-constrained environments. This paper introduces RepSegNet, a novel deep-learning model optimized for medical image segmentation. RepSegNet integrates convolutional neural networks with reparameterization techniques, effectively capturing both local and long-range features while simplifying complex structures during inference. Extensive experiments on diverse medical imaging datasets demonstrate RepSegNet’s superior performance over state-of-the-art models in key segmentation metrics. The model’s lightweight architecture ensures scalability and real-time applicability on edge devices, significantly reducing parameters and computational cost during inference. RepSegNet represents a significant advancement in medical image segmentation, offering a robust, efficient, and scalable solution across diverse clinical applications. Its ability to maintain high accuracy while reducing computational demands paves the way for improved diagnostic processes and potential integration into real-time medical imaging systems. Comprehensive ablation studies validate both architectural components, with reparameterization providing 80.5% parameter reduction and MultiPathMobileBlocks contributing 8.7 F1 points average improvement across all medical imaging modalities.