Accurate and efficient medical image segmentation is crucial for advancing clinical diagnostics and surgical planning, yet remains a complex challenge due to the variability in anatomical structures and the demand for low-complexity models. In this paper, we introduced Med-2D SegNet, a novel and highly efficient segmentation architecture that delivers outstanding accuracy while maintaining a minimal computational footprint. Med-2D SegNet achieves state-of-the-art performance across multiple benchmark datasets, including KVASIR-SEG, PH2, EndoVis, and GLAS, with an average Dice similarity coefficient (DSC) of 89.77% across 20 diverse datasets. Central to its success is the compact Med Block, a specialized encoder design that incorporates dimension expansion and parameter reduction, enabling precise feature extraction while keeping model parameters to a low count of just 2.07 million. Med-2D SegNet excels in cross-dataset generalization, particularly in polyp segmentation, where it was trained on KVASIR-SEG and showed strong performance on unseen datasets, demonstrating its robustness in zero-shot learning scenarios, even though we acknowledge that further improvements are possible. With top-tier performance in both binary and multi-class segmentation, Med-2D SegNet redefines the balance between accuracy and efficiency, setting a new benchmark for medical image analysis. This work paves the way for developing accessible, high-performance diagnostic tools suitable for clinical environments and resource-constrained settings, making it a step forward in the democratization of advanced medical technology.

翻译：准确且高效的医学图像分割对于推进临床诊断和手术规划至关重要，但由于解剖结构的多样性以及对低复杂度模型的需求，这仍然是一项复杂的挑战。本文介绍了Med-2D SegNet，这是一种新颖且高效的图像分割架构，它在保持极低计算开销的同时，提供了卓越的准确性。Med-2D SegNet在多个基准数据集上实现了最先进的性能，这些数据集包括KVASIR-SEG、PH2、EndoVis和GLAS，在20个不同的数据集上平均Dice相似系数（DSC）达到89.77%。其成功的核心在于紧凑的Med Block，这是一种专门的编码器设计，融合了维度扩展和参数缩减技术，能够在实现精确特征提取的同时，将模型参数量保持在仅207万的极低水平。Med-2D SegNet在跨数据集泛化方面表现出色，特别是在息肉分割任务中。该模型在KVASIR-SEG数据集上训练，并在未见过的数据集上展现出强大的性能，证明了其在零样本学习场景下的鲁棒性，尽管我们承认其仍有进一步改进的空间。凭借在二值分割和多类别分割任务中的顶级性能，Med-2D SegNet重新定义了准确性与效率之间的平衡，为医学图像分析设立了新的基准。这项工作为开发适用于临床环境和资源受限场景的、可及的高性能诊断工具铺平了道路，是推动先进医疗技术普及化向前迈进的一步。