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乳腺肿瘤影像分割：基于U-Net的研究与发展综述
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Abstract:
在乳腺癌的诊断与治疗过程中，乳腺肿瘤影像分割技术扮演着至关重要的角色，其精确度直接关系到病理分析的准确性及临床决策的有效性。近年来，U-Net及其改进模型在乳腺影像分割领域取得了显著的进展。U-Net的编码–解码结构和跳跃连接设计在提取多尺度特征和保持分辨率方面展现出独特优势，已发展成为医学图像分割领域的经典方法。随着研究的不断深入，针对U型网络的多方面优化进一步提升了其在乳腺医学图像分割中的性能。此外，U-Net在多模态影像分割任务中的应用也逐渐扩展。本文综述了基于U-Net的乳腺肿瘤分割模型的研究现状，探讨了其在数据集构建、性能评估指标、网络结构优化以及实际应用中的最新进展，并分析了当前研究面临的挑战和未来发展方向。该综述旨在为乳腺肿瘤影像分割领域的研究和应用提供重要的参考。
Breast tumor image segmentation is a pivotal technology in the diagnosis and treatment of breast cancer, with segmentation accuracy directly influencing subsequent pathological analysis and clinical decision-making. In recent years, U-Net and its improved models have achieved significant advancements in the field of breast image segmentation. The encoding-decoding structure and skip connection design of U-Net offer unique advantages in extracting multi-scale features and maintaining resolution, establishing it as a classic method for medical image segmentation. As research progresses, various optimizations of the U-Net network have further enhanced its performance in breast medical image segmentation. Moreover, the application of U-Net in multi-modal image segmentation tasks has also gradually expanded. This paper provides a comprehensive review of the research status of U-Net-based breast tumor segmentation models, discussing the latest advancements in datasets, performance metrics, network structure improvements, and practical applications, while also analyzing current research challenges and future development directions. This review serves as an important reference for the research and application of breast tumor image segmentation.
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