摘 要
医学图像分割是精准医疗的重要环节，传统方法难以满足临床对精度和效率的需求。近年来深度学习凭借强大的特征提取能力为医学图像分割带来革新。本文旨在探讨深度学习在该领域的最新进展，聚焦于提升分割精度、鲁棒性和泛化能力。研究基于卷积神经网络（CNN）架构，引入了多种创新机制，如U-Net结构的改进、注意力机制的应用以及多模态数据融合技术，有效解决了小目标检测难、边界模糊等关键问题。实验结果表明，相较于传统算法，基于深度学习的方法在脑部MRI、肺部CT等多种医学影像上实现了更高的Dice系数和更低的Hausdorff距离，尤其在处理复杂病理结构时表现出色。此外，通过迁移学习和半监督学习策略，模型能够在有限标注数据条件下保持良好性能。本研究不仅验证了深度学习在医学图像分割中的优越性，还为临床应用提供了可靠的技术支持，推动了智能医疗的发展进程，为后续研究奠定了坚实基础。
关键词：深度学习 医学图像分割 卷积神经网络
Abstract
Medical image segmentation is a critical component of precision medicine, where traditional methods struggle to meet the clinical demands for accuracy and efficiency. In recent years, deep learning has revolutionized medical image segmentation through its powerful feature extraction capabilities. This paper explores the latest advancements of deep learning in this field, focusing on improving segmentation accuracy, robustness, and generalization. Based on convolutional neural network (CNN) architectures, various innovative mechanisms have been introduced, such as improvements to the U-Net structure, application of attention mechanisms, and multimodal data fusion techniques, effectively addressing key challenges like difficult small ob ject detection and ambiguous boundaries. Experimental results demonstrate that, compared with traditional algorithms, deep learning-based methods achieve higher Dice coefficients and lower Hausdorff distances in various medical images including brain MRI and lung CT, particularly excelling in handling complex pathological structures. Furthermore, by employing transfer learning and semi-supervised learning strategies, the models maintain good performance under conditions of limited annotated data. This study not only verifies the superiority of deep learning in medical image segmentation but also provides reliable technical support for clinical applications, promoting the development of intelligent healthcare and laying a solid foundation for future research.
Keyword:Deep Learning Medical Image Segmentation Convolutional Neural Network
目 录
引言 1
1深度学习模型发展概述 1
1.1医学图像分割需求分析 1
1.2深度学习模型演进历程 2
1.3关键技术突破与应用 2
2数据集构建与标注方法 3
2.1医学图像数据特点 3
2.2标注工具与质量控制 3
2.3数据增强技术应用 4
3算法优化与性能提升 5
3.1损失函数设计改进 5
3.2模型架构创新探索 5
3.3训练策略优化实践 6
4临床应用与未来展望 6
4.1典型应用场景分析 6
4.2实际应用效果评估 7
4.3发展趋势与挑战应对 7
结论 8
参考文献 9
致谢 9
