Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (2): 589-594.DOI: 10.11772/j.issn.1001-9081.2021122147
• Multimedia computing and computer simulation • Previous Articles
Shaokang XU1, Zhancheng ZHANG1,2(), Haonan YAO1, Zhiwei ZOU1, Baocheng ZHANG3
Received:
2021-12-22
Revised:
2022-07-19
Accepted:
2022-07-20
Online:
2022-09-23
Published:
2023-02-10
Contact:
Zhancheng ZHANG
About author:
XU Shaokang, born in 1997, M. S. candidate. His research interests include computer vision, deep learning, medical image registration.Supported by:
徐少康1, 张战成1,2(), 姚浩男1, 邹智伟1, 张宝成3
通讯作者:
张战成
作者简介:
徐少康(1997—),男,江苏淮安人,硕士研究生,主要研究方向:计算机视觉、深度学习、医学图像配准基金资助:
CLC Number:
Shaokang XU, Zhancheng ZHANG, Haonan YAO, Zhiwei ZOU, Baocheng ZHANG. 2D/3D spine medical image real-time registration method based on pose encoder[J]. Journal of Computer Applications, 2023, 43(2): 589-594.
徐少康, 张战成, 姚浩男, 邹智伟, 张宝成. 基于姿态编码器的2D/3D脊椎医学图像实时配准方法[J]. 《计算机应用》唯一官方网站, 2023, 43(2): 589-594.
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URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2021122147
部位 | 部位 | ||||||
---|---|---|---|---|---|---|---|
1~3节 | 1.1 | 1.1 | 1.4 | 6~9节 | 0.9 | 0.9 | 1.1 |
4~5节 | 0.9 | 0.9 | 1.2 | 10~12节 | 0.9 | 0.9 | 1.1 |
Tab. 1 Average rotation angle error of spine at different parts
部位 | 部位 | ||||||
---|---|---|---|---|---|---|---|
1~3节 | 1.1 | 1.1 | 1.4 | 6~9节 | 0.9 | 0.9 | 1.1 |
4~5节 | 0.9 | 0.9 | 1.2 | 10~12节 | 0.9 | 0.9 | 1.1 |
方法 | MAE | mTRE/mm | 成功率/% | 时间/s |
---|---|---|---|---|
本文方法 | 0.04 | 1.16 | 98.0 | 1.7 |
Opt-NGI[ | 0.03+0.29 | 1.13+0.21 | 72.0 | 121.8 |
Opt-GC[ | 0.09+0.19 | 2.51+0.11 | 66.0 | 114.3 |
Opt-GO[ | 0.10+0.18 | 2.53+0.13 | 56.0 | 111.9 |
Bresenham[ | 0.03+0.33 | 1.45+0.18 | 94.0 | 89.6 |
Fourier[ | 0.05+0.41 | 1.24+0.25 | 86.0 | 17.5 |
MLP[ | 2.45+0.004 | 3.40+0.001 | 58.0 | 2.9 |
Tab. 2 Comparison of experimental results of the proposed method and other methods
方法 | MAE | mTRE/mm | 成功率/% | 时间/s |
---|---|---|---|---|
本文方法 | 0.04 | 1.16 | 98.0 | 1.7 |
Opt-NGI[ | 0.03+0.29 | 1.13+0.21 | 72.0 | 121.8 |
Opt-GC[ | 0.09+0.19 | 2.51+0.11 | 66.0 | 114.3 |
Opt-GO[ | 0.10+0.18 | 2.53+0.13 | 56.0 | 111.9 |
Bresenham[ | 0.03+0.33 | 1.45+0.18 | 94.0 | 89.6 |
Fourier[ | 0.05+0.41 | 1.24+0.25 | 86.0 | 17.5 |
MLP[ | 2.45+0.004 | 3.40+0.001 | 58.0 | 2.9 |
Metric | mTRE/mm | MAE |
---|---|---|
2.80+0.008 | 0.18+0.040 | |
2.56+0.009 | 0.16+0.035 | |
2.11+0.013 | 0.13+0.031 | |
2.73+0.009 | 0.19+0.023 | |
1.98+0.019 | 0.19+0.027 | |
1.93+0.021 | 0.11+0.038 | |
1.10 | 0.05 |
Tab. 3 Similarity results of different loss training
Metric | mTRE/mm | MAE |
---|---|---|
2.80+0.008 | 0.18+0.040 | |
2.56+0.009 | 0.16+0.035 | |
2.11+0.013 | 0.13+0.031 | |
2.73+0.009 | 0.19+0.023 | |
1.98+0.019 | 0.19+0.027 | |
1.93+0.021 | 0.11+0.038 | |
1.10 | 0.05 |
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