# 基于深度神经网络的尺桡骨远端图像语义分割DRU Image Semantic Segmentation Using Deep Neural Networks

DOI: 10.12677/JISP.2018.72010, PDF, HTML, XML, 下载: 978  浏览: 2,048  科研立项经费支持

Abstract: Abstract Semantic segmentation of the distal radius and ulna images can extract the region of interest (ROI) for Ulnar radius, which counts a great deal for bone age recognition. In this work, we propose a fully convolutional network based model for semantic segmentation. Experimental results show that the proposed model can precisely segment the ulna and radius from distal radius and ulna images. We also discussed the influence of different network structures. For ulna, we get an accuracy of 97%, recall of 97%, IoU of 95%. For radius, we get an accuracy of 98.5%, recall of 98%, IoU of 96.6%.

1. 引言

1) 首次提出使用FCN分割DRU图像，并达到较好的分割效果。

2) 通过实验对比不同的FCN结构和相同结构下不同数量的训练样本对分割精度的影响。

2. 方法描述

2.1. 全卷积神经网络与语义分割

2.2. 网络结构

Figure 1. Image semantic segmentation flow chart

Figure 2. Network structure diagram

3. 实验与分析

3.1. 实验数据

3.2. 数据处理

3.3. 数据标注

3.4. 图像分割评价指标

3.4.1. 精确度和召回率

Figure 3. X-ray image of DRU

Figure 4. Images after preprocessed

Figure 5. Data annotation

1) 伪阳性(FP)：预测为阳性，真实值为阴性；

2) 真阳性(TP)：预测为阳性，真实值为阳性；

3) 真阴性(TN)：预测为阴性，真实值为阴性；

4) 伪阴性(FN)：预测为阴性，真实值为阳性。

3.4.2. 交并比(IoU)

4. 实验结果

Figure 6. Graphical representation of IoU

Figure 7. Train loss varies with the number of iterations increases

(a) (b) (c)

Figure 8. Test accuracy varies with the number of iterations increases

Figure 9. Test loss varies with the number of iterations increases

5. 总结

NOTES

*通讯作者。

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