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<p>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.</p>

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Efficient medical image segmentation using RepSegNet lightweight reparameterized neural network

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年1月9日

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  • Rashid Juraev,
  • Il-Min Kim,
  • Sangseok Yun,
  • Jae-Mo Kang

摘要

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.