标题： 医学图像压缩综述：从传统方法到基于学习的方法 显示英文标题

标题： A Survey on Medical Image Compression: From Traditional to Learning-Based

摘要： 医学影像的指数增长给医疗系统在数据存储、传输和管理方面带来了重大挑战。 在这方面，高效的压缩变得越来越重要。 与自然图像压缩不同，医学图像压缩优先保留诊断细节和结构完整性，对质量要求更严格，并需要快速、内存高效的算法，在计算复杂度与临床上可接受的重建质量之间取得平衡。 同时，医学影像家族包括多种成像方式，每种方式都有不同的需求。 例如，2D医学图像（如X光片、组织病理学图像）的压缩侧重于利用切片内的空间冗余，而体积医学图像则需要处理切片内和切片间的空间相关性，4D动态成像（如时间序列CT/MRI、4D超声）还需要处理连续时间帧之间的时序相关性。 传统的压缩方法基于数学变换和信息论原理，提供了坚实的理论基础、可预测的性能和高水平的标准化，已在临床环境中得到广泛验证。 相比之下，基于深度学习的方法表现出卓越的自适应学习能力，能够捕捉医学图像中的复杂统计特征和语义信息。 本综述建立了一个基于数据结构（2D vs 3D/4D）和技术方法（传统 vs 基于学习）的双面分类法，从而系统地展示了完整的技术演变，分析了独特的技术挑战，并展望了医学图像压缩的未来方向。 显示英文摘要

摘要： The exponential growth of medical imaging has created significant challenges in data storage, transmission, and management for healthcare systems. In this vein, efficient compression becomes increasingly important. Unlike natural image compression, medical image compression prioritizes preserving diagnostic details and structural integrity, imposing stricter quality requirements and demanding fast, memory-efficient algorithms that balance computational complexity with clinically acceptable reconstruction quality. Meanwhile, the medical imaging family includes a plethora of modalities, each possessing different requirements. For example, 2D medical image (e.g., X-rays, histopathological images) compression focuses on exploiting intra-slice spatial redundancy, while volumetric medical image faces require handling intra-slice and inter-slice spatial correlations, and 4D dynamic imaging (e.g., time-series CT/MRI, 4D ultrasound) additionally demands processing temporal correlations between consecutive time frames. Traditional compression methods, grounded in mathematical transforms and information theory principles, provide solid theoretical foundations, predictable performance, and high standardization levels, with extensive validation in clinical environments. In contrast, deep learning-based approaches demonstrate remarkable adaptive learning capabilities and can capture complex statistical characteristics and semantic information within medical images. This comprehensive survey establishes a two-facet taxonomy based on data structure (2D vs 3D/4D) and technical approaches (traditional vs learning-based), thereby systematically presenting the complete technological evolution, analyzing the unique technical challenges, and prospecting future directions in medical image compression.