Segmentation method of myocardial perfusion bull-eye for the degree of loss of cardiac ischemia_Journal of Biomedical Engineering

Authors：

WANG He ¹ , ZHANG Ruyi ² ,  MENG Zhaowei ² , ZHU Shan ³ , ZHANG Wei ¹

1. School of Microelectronics, Tianjin University, Tianjin 300072, P.R.China;
2. Department of Nuclear Medicine, Tianjin Medical University General Hospital, Tianjin 300052, P.R.China;
3. Tianjin Hospital, Tianjin 300211, P.R. China;

Corresponding author：

MENG Zhaowei, Email: zmeng@tmu.edu.cn

Keywords：

myocardial perfusion bull-eye; cardiac ischemia; semantic segmentation; channel attention

DOI：

10.7507/1001-5515.202102019

Video：

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Abstract Full text Figures/Tables Video References Cited by

As one of the non-invasive imaging techniques, myocardial perfusion imaging provides a basis for the diagnosis of myocardial ischemia in coronary heart disease. Aiming at the bull-eye image in myocardial perfusion imaging, this paper proposed a branching structure, which included multi-layer transposed convolution up-sampling concatenate module and four-channel weighted channels attention module, and the output results of the branch structure were fused with the output results of trunk U-Net, to achieve accurate segmentation of the cardiac ischemia missing degree in myocardial perfusion bull-eye image. The experimental results show that the multi-layer transposed convolution up-sampling concatenate module realizes the fusion of different depth feature maps, and effectively reduces the interference of the severe sparse degree which is similar to the missing degree on the segmentation. Four-channel weighted attention module can further improve the ability to distinguish between the two similar degrees and the ability to learn edge details of the targets, and retain more abundant edge details features. The experimental data came from Tianjin Medical University General Hospital, Tianjin TEDA Hospital, Tianjin First Central Hospital and Third Central Hospital. The Jaccard scores in the self-built dataset was 5.00% higher than that of U-Net. The model presented in this paper is superior to other optimized models based on U-Net, and the subjective evaluation meets the accuracy requirements for clinical diagnosis.

Citation： WANG He, ZHANG Ruyi, MENG Zhaowei, ZHU Shan, ZHANG Wei. Segmentation method of myocardial perfusion bull-eye for the degree of loss of cardiac ischemia. Journal of Biomedical Engineering, 2021, 38(6): 1072-1080. doi: 10.7507/1001-5515.202102019 Copy

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7.	Haddad M, Adlassnig K P, Porenta G. Feasibility analysis of a case-based reasoning system for automated detection of coronary heart disease from myocardial scintigrams. Artif Intell Med, 1997, 9(1): 61-78.
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13.	Alonso D H, Wernick M N, Yang Yongyi, et al. Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning. Journal of Nuclear Cardiology, 2019, 26(5): 1746-1754.
14.	Berkaya S K, Siverikoz I A, Gunal S. Classification models for SPECT myocardial perfusion imaging. Computers in Biology and Medicine, 2020, 123: 103893.
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20.	Wang Fei, Jiang Mengqing, Qian Chen, et al. Residual attention network for image classification//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Hawaii: IEEE, 2017: 6450-6458.
21.	Wang Xiaolong, Girshick R, Gupta A, et al. Non-local neural networks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018: 7794-7803.
22.	Huang Zilong, Wang Xinggang, Huang Lichao, et al. CCNet: criss-cross attention for semantic segmentation//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE, 2019: 603-612.
23.	Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Hawaii: IEEE, 2017: 4700-4708.
24.	Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Athens: Springer, 2016: 424-432.
25.	Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: learning where to look for the pancreas, arXiv preprint arXiv, 2018: 1804.03999.
26.	Li X, Chen H, Qi X, et al. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging, 2018, 37(12): 2663-2674.
27.	Ibtehaz N, Rahman M S. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 2020, 121: 74-87.
28.	Xie Saining, Tu Zhuowen. Holistically-Nested edge detection//2015 IEEE International Conference on Computer Vision (ICCV). Santiago: IEEE, 2015: 1395-1403.
29.	Woo S, Park J, Lee J Y, et al. CBAM: Convolutional block attention module//2018 European Conference on Computer Vision (ECCV). Munich: CVF, 2018: 3-19.
30.	Jaccard P. The distribution of the flora in the alpine zone. 1. New phytologist, 1912, 11(2): 37-50.

1. 王增武, 胡盛寿. 《中国心血管健康与疾病报告2019》要点解读. 中国心血管杂志, 2020, 25(5): 401-410.
2. Benjamin E J, Muntner P, Alonso A A, et al. Heart disease and stroke statistics-2019 update a report from the American heart association. Circulation, 2019, 139(10): E56-E528.
3. Notghi A, Low C S. Myocardial perfusion scintigraphy: past, present and future. The British journal of radiology, 2011, 84 (Spec Iss 3): S229-S236.
4. Underwood S R, Godman B, Salyani S, et al. Economics of myocardial perfusion imaging in Europe-the EMPIRE Study. Eur Heart J, 1999, 20(2): 157-166.
5. Dondi M, Bastos F M. Nuclear cardiology: its role in cost effective care. IAEA human health series, 2012, 18: 2075-3772.
6. Alexanderson E, Better N, Bouyoucef S E, et al. Nuclear cardiology: guidance on the implementation of SPECT myocardial perfusion imaging. IAEA Human Health Series, 2016, 23: 101.
7. Haddad M, Adlassnig K P, Porenta G. Feasibility analysis of a case-based reasoning system for automated detection of coronary heart disease from myocardial scintigrams. Artif Intell Med, 1997, 9(1): 61-78.
8. Khorsand A, Haddad M, Graf S, et al. Automated assessment of dipyridamole 201Tl myocardial SPECT perfusion scintigraphy by case-based reasoning. J Nucl Med, 2001, 42(2): 189-193.
9. Garcia E V, Cooke C D, Folks R D, et al. Diagnostic performance of an expert system for the interpretation of myocardial perfusion SPECT studies. J Nucl Med, 2001, 42(8): 1185-1191.
10. Jamzad M, Uchiyama A, Toyama H, et al. Analysis of thallium-201 myocardial SPECT images using fuzzy set theory. Ann Nucl Med, 1988, 2(2): 63-71.
11. Niemeyer M G, van der Wall E E, Kuyper A F, et al. Discordance of visual and quantitative analysis regarding false negative and false positive test results in thallium-201 myocardial perfusion scintigraphy. Am J Physiol Imaging, 1991, 6(1): 34-43.
12. Rahmani R, Niazi P, Naseri M, et al. Improved diagnostic accuracy for myocardial perfusion imaging using artificial neural networks on different input variables including clinical and quantification data. Revista Española de Medicina Nuclear e Imagen Molecular (English Edition), 2019, 38(5): 275-279.
13. Alonso D H, Wernick M N, Yang Yongyi, et al. Prediction of cardiac death after adenosine myocardial perfusion SPECT based on machine learning. Journal of Nuclear Cardiology, 2019, 26(5): 1746-1754.
14. Berkaya S K, Siverikoz I A, Gunal S. Classification models for SPECT myocardial perfusion imaging. Computers in Biology and Medicine, 2020, 123: 103893.
15. 李思进. SPECT心肌灌注显像技术与图像处理要点专家共识(2019版). 中华核医学与分子影像杂志, 2020, 2020(01): 32-36.
16. Ronneberger O, Fischer P, Brox T. U-net: convolutional networks for biomedical image segmentation //International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Munich: Springer, 2015: 234-241.
17. He Kaiming, Zhang Xiangyu, Ren Shaoqing, et al. Deep residual learning for image recognition//2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Las Vegas: IEEE, 2016: 770-778.
18. Xie Saining, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Hawaii: IEEE, 2017: 5987-5995.
19. Vaswani A, Shazeer N, Parmar N, et al. Attention is all you need//Advances in Neural Information Processing Systems (NIPS). Long Beach: NIPS, 2017: 5998-6008.
20. Wang Fei, Jiang Mengqing, Qian Chen, et al. Residual attention network for image classification//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) . Hawaii: IEEE, 2017: 6450-6458.
21. Wang Xiaolong, Girshick R, Gupta A, et al. Non-local neural networks//2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). Salt Lake City: IEEE, 2018: 7794-7803.
22. Huang Zilong, Wang Xinggang, Huang Lichao, et al. CCNet: criss-cross attention for semantic segmentation//2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE, 2019: 603-612.
23. Huang G, Liu Z, van der Maaten L, et al. Densely connected convolutional networks//2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Hawaii: IEEE, 2017: 4700-4708.
24. Çiçek Ö, Abdulkadir A, Lienkamp S S, et al. 3D U-Net: learning dense volumetric segmentation from sparse annotation//International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). Athens: Springer, 2016: 424-432.
25. Oktay O, Schlemper J, Folgoc L L, et al. Attention U-net: learning where to look for the pancreas, arXiv preprint arXiv, 2018: 1804.03999.
26. Li X, Chen H, Qi X, et al. H-DenseUNet: hybrid densely connected UNet for liver and tumor segmentation from CT volumes. IEEE Trans Med Imaging, 2018, 37(12): 2663-2674.
27. Ibtehaz N, Rahman M S. MultiResUNet: rethinking the U-Net architecture for multimodal biomedical image segmentation. Neural Networks, 2020, 121: 74-87.
28. Xie Saining, Tu Zhuowen. Holistically-Nested edge detection//2015 IEEE International Conference on Computer Vision (ICCV). Santiago: IEEE, 2015: 1395-1403.
29. Woo S, Park J, Lee J Y, et al. CBAM: Convolutional block attention module//2018 European Conference on Computer Vision (ECCV). Munich: CVF, 2018: 3-19.
30. Jaccard P. The distribution of the flora in the alpine zone. 1. New phytologist, 1912, 11(2): 37-50.

Journal of Biomedical Engineering

Segmentation method of myocardial perfusion bull-eye for the degree of loss of cardiac ischemia

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