深度学习在医学图像分割中的算法改进
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高精度、高效性分割的需求。深度学习凭借强大的特征提取能力为医学图像分割带来新机遇。本研究旨在改进深度学习在医学图像分割中的算法，以提高分割精度和效率。针对现有深度学习分割算法存在边缘细节丢失、小目标检测困难等问题，提出一种融合多尺度特征聚合与注意力机制的新型网络架构。该架构通过构建多尺度特征金字塔，在不同层次上聚合特征信息，增强对不同尺度目标的表达能力；引入空间注意力机制聚焦于关键区域，强化重要特征并抑制冗余信息。实验选取多种典型医学图像数据集进行测试，结果表明所提算法在Dice系数、Jaccard指数等评价指标上均优于传统深度学习分割算法，尤其在处理微小病变区域时优势明显。
关键词：医学图像分割 深度学习 多尺度特征聚合
Abstract
Medical image segmentation is an important part of precision medicine, and the traditional method is difficult to meet the clinical demand for high-precision and efficient segmentation. Deep learning brings new opportunities for medical image segmentation with its powerful feature extraction ability. This study aims to improve the algorithm of deep learning in medical image segmentation to improve segmentation accuracy and efficiency. Aiming at the existing deep learning segmentation algorithm problems, such as edge detail loss and small ob ject detection difficulty, a new network architecture integrating multi-scale feature aggregation and attention mechanism is proposed. By constructing multi-scale feature pyramid, the architecture gathers feature information at different levels and enhances the ex pression ability of targets at different scales; introduces spatial attention mechanism to focus on key areas, strengthens important features and suppresses redundant information. Various typical medical image data sets are selected for testing, and the results show that the proposed algorithm is better than the traditional deep learning segmentation algorithm in the evaluation indexes such as Dice coefficient and Jaccard index, especially in dealing with small lesions.
Keyword:Medical Image Segmentation Deep Learning Multi-scale Feature Aggregation
目 录
1绪论 1
1.1深度学习与医学图像分割背景 1
1.2算法改进的研究意义 1
1.3国内外研究现状综述 1
1.4本文研究方法概述 2
2医学图像分割算法分析 2
2.1常用深度学习模型评估 2
2.2现有算法的局限性 3
2.3关键技术难点剖析 3
3算法改进方案设计 4
3.1改进思路与目标 4
3.2新型网络架构构建 4
3.3损失函数优化策略 5
3.4数据增强方法探索 6
4实验验证与结果分析 6
4.1实验环境搭建 6
4.2性能对比测试 7
4.3结果可视化展示 7
结论 8
参考文献 10
致谢 11
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高精度、高效性分割的需求。深度学习凭借强大的特征提取能力为医学图像分割带来新机遇。本研究旨在改进深度学习在医学图像分割中的算法，以提高分割精度和效率。针对现有深度学习分割算法存在边缘细节丢失、小目标检测困难等问题，提出一种融合多尺度特征聚合与注意力机制的新型网络架构。该架构通过构建多尺度特征金字塔，在不同层次上聚合特征信息，增强对不同尺度目标的表达能力；引入空间注意力机制聚焦于关键区域，强化重要特征并抑制冗余信息。实验选取多种典型医学图像数据集进行测试，结果表明所提算法在Dice系数、Jaccard指数等评价指标上均优于传统深度学习分割算法，尤其在处理微小病变区域时优势明显。
关键词：医学图像分割 深度学习 多尺度特征聚合
Abstract
Medical image segmentation is an important part of precision medicine, and the traditional method is difficult to meet the clinical demand for high-precision and efficient segmentation. Deep learning brings new opportunities for medical image segmentation with its powerful feature extraction ability. This study aims to improve the algorithm of deep learning in medical image segmentation to improve segmentation accuracy and efficiency. Aiming at the existing deep learning segmentation algorithm problems, such as edge detail loss and small ob ject detection difficulty, a new network architecture integrating multi-scale feature aggregation and attention mechanism is proposed. By constructing multi-scale feature pyramid, the architecture gathers feature information at different levels and enhances the ex pression ability of targets at different scales; introduces spatial attention mechanism to focus on key areas, strengthens important features and suppresses redundant information. Various typical medical image data sets are selected for testing, and the results show that the proposed algorithm is better than the traditional deep learning segmentation algorithm in the evaluation indexes such as Dice coefficient and Jaccard index, especially in dealing with small lesions.
Keyword:Medical Image Segmentation Deep Learning Multi-scale Feature Aggregation
目 录
1绪论 1
1.1深度学习与医学图像分割背景 1
1.2算法改进的研究意义 1
1.3国内外研究现状综述 1
1.4本文研究方法概述 2
2医学图像分割算法分析 2
2.1常用深度学习模型评估 2
2.2现有算法的局限性 3
2.3关键技术难点剖析 3
3算法改进方案设计 4
3.1改进思路与目标 4
3.2新型网络架构构建 4
3.3损失函数优化策略 5
3.4数据增强方法探索 6
4实验验证与结果分析 6
4.1实验环境搭建 6
4.2性能对比测试 7
4.3结果可视化展示 7
结论 8
参考文献 10
致谢 11
