Journal of Computer Applications ›› 2023, Vol. 43 ›› Issue (5): 1596-1605.DOI: 10.11772/j.issn.1001-9081.2022040536
• Multimedia computing and computer simulation • Previous Articles
Jinwen GUO1, Xinghua MA1, Gongning LUO1, Wei WANG1, Yang CAO2, Kuanquan WANG1()
Received:
2022-04-21
Revised:
2022-07-05
Accepted:
2022-07-08
Online:
2022-08-05
Published:
2023-05-10
Contact:
Kuanquan WANG
About author:
GUO Jinwen, born in 1997, M. S. candidate. His research interests include medical image processing, computer vision.Supported by:
郭劲文1, 马兴华1, 骆功宁1, 王玮1, 曹阳2, 王宽全1()
通讯作者:
王宽全
作者简介:
郭劲文(1997—),男,宁夏银川人,硕士研究生,主要研究方向:医学图像处理、计算机视觉基金资助:
CLC Number:
Jinwen GUO, Xinghua MA, Gongning LUO, Wei WANG, Yang CAO, Kuanquan WANG. Guidewire artifact removal method of structure-enhanced IVOCT based on Transformer[J]. Journal of Computer Applications, 2023, 43(5): 1596-1605.
郭劲文, 马兴华, 骆功宁, 王玮, 曹阳, 王宽全. 基于Transformer的结构强化IVOCT导丝伪影去除方法[J]. 《计算机应用》唯一官方网站, 2023, 43(5): 1596-1605.
Add to citation manager EndNote|Ris|BibTeX
URL: http://www.joca.cn/EN/10.11772/j.issn.1001-9081.2022040536
损失函数 | 参数名称 | 参数值 |
---|---|---|
缺失区域与未缺失区域 | 1.00 | |
1.00 | ||
ORI与RTV | 0.20 | |
0.20 | ||
重建与对抗 | 1.00 | |
0.01 |
Tab. 1 Parameter values of loss function
损失函数 | 参数名称 | 参数值 |
---|---|---|
缺失区域与未缺失区域 | 1.00 | |
1.00 | ||
ORI与RTV | 0.20 | |
0.20 | ||
重建与对抗 | 1.00 | |
0.01 |
方法 | PSNR/dB | SSIM/% | MAE | FID |
---|---|---|---|---|
VINet[ | 32.32 | 93.79 | 0.017 5 | 23.87 |
LGTSM[ | 32.85 | 94.58 | 0.017 2 | 16.37 |
CAP[ | 34.68 | 95.60 | 0.016 8 | 18.26 |
STTN[ | 35.21 | 96.08 | 0.016 4 | 10.75 |
SETNTR- | 27.16 | 92.02 | 0.018 2 | 65.05 |
SETNRTV- | 35.51 | 96.11 | 0.016 2 | 10.99 |
SETN | 36.03 | 96.25 | 0.015 4 | 9.32 |
Tab. 2 Evaluation results of guidewire artifact removal
方法 | PSNR/dB | SSIM/% | MAE | FID |
---|---|---|---|---|
VINet[ | 32.32 | 93.79 | 0.017 5 | 23.87 |
LGTSM[ | 32.85 | 94.58 | 0.017 2 | 16.37 |
CAP[ | 34.68 | 95.60 | 0.016 8 | 18.26 |
STTN[ | 35.21 | 96.08 | 0.016 4 | 10.75 |
SETNTR- | 27.16 | 92.02 | 0.018 2 | 65.05 |
SETNRTV- | 35.51 | 96.11 | 0.016 2 | 10.99 |
SETN | 36.03 | 96.25 | 0.015 4 | 9.32 |
方法 | PA | MPA | IoU | DICE | Precision | Recall |
---|---|---|---|---|---|---|
未处理 | 94.67 | 92.15 | 86.95 | 84.31 | 91.60 | 87.41 |
STTN[ | 98.20 | 97.20 | 95.05 | 94.11 | 96.82 | 95.42 |
SETNRTV- | 98.58 | 97.19 | 95.57 | 94.69 | 97.68 | 95.17 |
SETN | 98.59 | 97.29 | 95.72 | 95.24 | 95.22 | 95.22 |
Tab. 3 Segmentation results of different input
方法 | PA | MPA | IoU | DICE | Precision | Recall |
---|---|---|---|---|---|---|
未处理 | 94.67 | 92.15 | 86.95 | 84.31 | 91.60 | 87.41 |
STTN[ | 98.20 | 97.20 | 95.05 | 94.11 | 96.82 | 95.42 |
SETNRTV- | 98.58 | 97.19 | 95.57 | 94.69 | 97.68 | 95.17 |
SETN | 98.59 | 97.29 | 95.72 | 95.24 | 95.22 | 95.22 |
1 | 中国心血管健康与疾病报告编写组. 中国心血管健康与疾病报告2020概要[J]. 中国循环杂志, 2021, 36(6):521-545. 10.3969/j.issn.1000-3614.2021.06.001 |
The Writing Committee of the Report on Cardiovascular Health and Diseases in China. Report on cardiovascular health and diseases burden in China: an updated summary of 2020[J]. Chinese Circulation Journal, 2021, 36(6): 521-545. 10.3969/j.issn.1000-3614.2021.06.001 | |
2 | 韩涛,邱建榕,王迪,等. 光学相干层析显微成像的技术与应用[J]. 中国激光, 2020, 47(2): No.0207004. 10.3788/cjl202047.0207004 |
HAN T, QIU J R, WANG D, et al. Optical coherence microscopy and its application[J]. Chinese Journal of Lasers, 2020, 47(2): No.0207004. 10.3788/cjl202047.0207004 | |
3 | 陆冬筱,房文汇,李玉瑶,等. 光学相干层析成像技术原理及研究进展[J]. 中国光学, 2020, 13(5):919-935. 10.37188/CO.2020-0037 |
LU D X, FANG W H, LI Y Y, et al. Optical coherence tomography: principles and recent developments[J]. Chinese Optics, 2020, 13(5):919-935. 10.37188/CO.2020-0037 | |
4 | FERCHER A F, DREXLER W, HITZENBERGER C K, et al. Optical coherence tomography - principles and applications[J]. Reports on Progress in Physics, 2003, 66(2): No.239. 10.1088/0034-4885/66/2/204 |
5 | FERCHER A F. Optical coherence tomography - development, principles, applications[J]. Zeitschrift für Medizinische Physik, 2010, 20(4): 251-276. 10.1016/j.zemedi.2009.11.002 |
6 | JANG I K, BOUMA B E, KANG D H, et al. Visualization of coronary atherosclerotic plaques in patients using optical coherence tomography: comparison with intravascular ultrasound[J]. Journal of the American College of Cardiology, 2002, 39(4): 604-609. 10.1016/s0735-1097(01)01799-5 |
7 | TEARNEY G J, REGAR E, AKASAKA T, et al. Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies: a report from the international working group for intravascular optical coherence tomography standardization and validation[J]. Journal of the American College of Cardiology, 2012, 59(12): 1058-1072. 10.1016/j.jacc.2011.09.079 |
8 | REGAR E, AKASAKA T, ADRIAENSSENS T, et al. Consensus standards for acquisition, measurement, and reporting of intravascular optical coherence tomography studies[J]. Journal of the American College of Cardiology, 2012, 59: 1058-1072. |
9 | PRATI F, CERA M, RAMAZZOTTI V, et al. Safety and feasibility of a new non-occlusive technique for facilitated intracoronary Optical Coherence Tomography (OCT) acquisition in various clinical and anatomical scenarios[J]. EuroIntervention, 2007, 3(3): 365-370. 10.4244/eijv3i3a66 |
10 | XU L, YAN Q, XIA Y, et al. Structure extraction from texture via relative total variation[J]. ACM Transactions on Graphics, 2012, 31(6): No.139. 10.1145/2366145.2366158 |
11 | YU L F, LI H, MUELLER J, et al. Metal artifact reduction from reformatted projections for hip prostheses in multislice helical computed tomography: techniques and initial clinical results[J]. Investigative Radiology, 2009, 44(11): 691-696. 10.1097/rli.0b013e3181b0a2f9 |
12 | LIU J Y, YANG S, FANG Y M, et al. Structure-guided image inpainting using homography transformation[J]. IEEE Transactions on Multimedia, 2018, 20(12): 3252-3265. 10.1109/tmm.2018.2831636 |
13 | DING D, RAM S, RODRIGUEZ J J. Image inpainting using nonlocal texture matching and nonlinear filtering[J]. IEEE Transactions on Image Processing, 2019, 28(4): 1705-1719. 10.1109/tip.2018.2880681 |
14 | LI H, LUO W, HUANG J. Localization of diffusion-based inpainting in digital images[J]. IEEE Transactions on Information Forensics and Security, 2017, 12(12): 3050-3064. 10.1109/tifs.2017.2730822 |
15 | SRIDEVI G, SRINIVAS KUMAR S. Image inpainting based on fractional-order nonlinear diffusion for image reconstruction[J]. Circuits, Systems, and Signal Processing, 2019, 38(8): 3802-3817. 10.1007/s00034-019-01029-w |
16 | XIE J Y, XU L L, CHEN E H. Image denoising and inpainting with deep neural networks[C]// Proceedings of the 25th International Conference on Neural Information Processing Systems — Volume 1. Red Hook, NY: Curran Associates Inc., 2012: 341-349. |
17 | FAVORSKAYA M, DAMOV M, ZOTIN A. Intelligent texture reconstruction of missing data in video sequences using neural networks[M]// TWEEDALE J W, JAIN, L C. Advanced Techniques for Knowledge Engineering and Innovative Applications: 16th International Conference, KES 2012, San Sebastian, Spain, September 10-12, 2012, Revised Selected Papers, CCIS 246. Berlin: Springer, 2013: 163-176. 10.1007/978-3-642-42017-7_12 |
18 | KÖHLER R, SCHULER C, SCHÖLKOPF B, et al. Mask-specific inpainting with deep neural networks[C]// Proceedings of the 2014 German Conference on Pattern Recognition, LNCS 8753. Cham: Springer, 2014: 523-534. |
19 | GOODFELLOW I J, POUGET-ABADIE J, MIRZA M, et al. Generative adversarial nets[C]// Proceedings of the 27th International Conference on Neural Information Processing Systems — Volume 2. Cambridge: MIT Press, 2014: 2672-2680. |
20 | KIM D, WOO S, LEE J Y, et al. Deep video inpainting[C]// Proceedings of the 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Piscataway: IEEE, 2019: 5785-5794. 10.1109/cvpr.2019.00594 |
21 | CHANG Y L, LIU Z Y, LEE K Y, et al. Free-form video inpainting with 3D gated convolution and Temporal PatchGAN[C]// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2019: 9065-9074. 10.1109/iccv.2019.00916 |
22 | LEE S, OH S W, WON D, et al. Copy-and-paste networks for deep video inpainting[C]// Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision. Piscataway: IEEE, 2019: 4412-4420. 10.1109/iccv.2019.00451 |
23 | VASWANI A, SHAZEER N, PARMAR N, et al. Attention is all you need[C]// Proceedings of the 31st International Conference on Neural Information Processing Systems. Red Hook, NY: Curran Associates Inc., 2017: 6000-6010. |
24 | DOSOVITSKIY A, BEYER L, KOLESNIKOV A, et al. An image is worth 16x16 words: Transformers for image recognition at scale[EB/OL]. (2021-06-03) [2022-03-22].. |
25 | JIANG Y F, CHANG S Y, WANG Z Y. TransGan: two pure transformers can make one strong GAN, and that can scale up[C/OL]// Proceedings of the 35th Conference on Neural Information Processing Systems [2022-03-22]. . |
26 | ZENG Y H, FU J L, CHAO H Y. Learning joint spatial-temporal transformations for video inpainting[C]// Proceedings of the 2020 European Conference on Computer Vision, LNCS 12361. Cham: Springer, 2020: 528-543. |
[1] | Liyao FU, Mengxiao YIN, Feng YANG. Transformer based U-shaped medical image segmentation network: a survey [J]. Journal of Computer Applications, 2023, 43(5): 1584-1595. |
[2] | Hao WANG, Zicheng WANG, Chao ZHANG, Yunsheng MA. Generative adversarial network based data uncertainty quantification method [J]. Journal of Computer Applications, 2023, 43(4): 1094-1101. |
[3] | Xiaoyu FAN, Suzhen LIN, Yanbo WANG, Feng LIU, Dawei LI. Reconstruction algorithm for highly undersampled magnetic resonance images based on residual graph convolutional neural network [J]. Journal of Computer Applications, 2023, 43(4): 1261-1268. |
[4] | Yongbing GAO, Juntian GAO, Rong MA, Lidong YANG. User granularity-level personalized social text generation model [J]. Journal of Computer Applications, 2023, 43(4): 1021-1028. |
[5] | Chunyong YIN, Liwen ZHOU. Unsupervised time series anomaly detection model based on re-encoding [J]. Journal of Computer Applications, 2023, 43(3): 804-811. |
[6] | Yongxiang GU, Xin LAN, Boyi FU, Xiaolin QIN. Object detection algorithm for remote sensing images based on geometric adaptation and global perception [J]. Journal of Computer Applications, 2023, 43(3): 916-922. |
[7] | Gang CHEN, Yongwei LIAO, Zhenguo YANG, Wenying LIU. Image inpainting algorithm of multi-scale generative adversarial network based on multi-feature fusion [J]. Journal of Computer Applications, 2023, 43(2): 536-544. |
[8] | Li’an ZHU, Hong ZHANG. Nonhomogeneous image dehazing based on dual-branch conditional generative adversarial network [J]. Journal of Computer Applications, 2023, 43(2): 567-574. |
[9] | Chengyu LIN, Lei WANG, Cong XUE. Weakly-supervised text classification with label semantic enhancement [J]. Journal of Computer Applications, 2023, 43(2): 335-342. |
[10] | Ming XU, Linhao LI, Qiaoling QI, Liqin WANG. Abductive reasoning model based on attention balance list [J]. Journal of Computer Applications, 2023, 43(2): 349-355. |
[11] | Ruoying WANG, Fan LYU, Liuqing ZHAO, Fuyuan HU. Floorplan generation algorithm integrating user requirements and boundary constraints [J]. Journal of Computer Applications, 2023, 43(2): 575-582. |
[12] | Lingling TAO, Bo LIU, Wenbo LI, Xiping HE. Controllable face editing algorithm with closed-form solution [J]. Journal of Computer Applications, 2023, 43(2): 601-607. |
[13] | Jiahang ZHOU, Hongjie XING. Novelty detection method based on dual autoencoders and Transformer network [J]. Journal of Computer Applications, 2023, 43(1): 22-29. |
[14] | Ziqi HU, Kai XIE, Chang WEN, Meiran LI, Jianbiao HE. Low dose CT image enhancement based on generative adversarial network [J]. Journal of Computer Applications, 2023, 43(1): 280-288. |
[15] | Zanxia QIANG, Xianfu BAO. Residual attention deraining network based on convolutional long short-term memory [J]. Journal of Computer Applications, 2022, 42(9): 2858-2864. |
Viewed | ||||||
Full text |
|
|||||
Abstract |
|
|||||