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A new segment method for pulmonary artery and vein

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Abstract

Accurate differentiation between pulmonary arteries and veins (A/V) holds pivotal importance in the realm of diagnosing and treating pulmonary ailments. This study presents a new approach that leverages grayscale differences between A/V. Distinctions are measured using median and mean grayscale values within the vessel area. Initially, adherent regions are removed based on vessel structure. The trunk regions are segmented using gray level information near the heart region of the lung boundary. Incorrectly segmented vessels are corrected based on connectivity. For distal lung vessels, a similar distance field is established using a graph-cut method. Experimental results show the algorithm’s superior segmentation accuracy, achieving 97.26% compared to the CNN-based average accuracy of 91.67%. Error branches are more concentrated, aiding subsequent manual and automatic correction. This demonstrates the algorithm’s effective segmentation of pulmonary A/V.

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Data availability

The data that support the findings of this study are available from the corresponding author upon reasonable request.

References

  1. Tellapuri S, Park HS, Kalva SP. Pulmonary arteriovenous malformations. Int J Cardiovasc Imaging. 2019;35(8):1421–8.

    Article  Google Scholar 

  2. Papagiannis J, Apostolopoulou S, Sarris G, Rammos S. Diagnosis and management of pulmonary arteriovenous malformations. Images Paediatr Cardiol. 2002;4(1):33–49.

    Google Scholar 

  3. Cummings KW, Bhalla S. Pulmonary vascular diseases. Clin Chest Med. 2015;36(2):235-248 viii.

    Article  Google Scholar 

  4. Zhou C, et al. Automatic multiscale enhancement and segmentation of pulmonary vessels in CT pulmonary angiography images for CAD applications. Med Phys. 2007;34(12):4567–77.

    Article  Google Scholar 

  5. Rahaghi FN, et al. Pulmonary vascular morphology as an imaging biomarker in chronic thromboembolic pulmonary hypertension. Pulm Circ. 2016;6(1):70–81.

    Article  Google Scholar 

  6. Sluimer I, Schilham A, Prokop M, van Ginneken B. Computer analysis of computed tomography scans of the lung: a survey. IEEE Trans Med Imaging. 2006;25(4):385–405.

    Article  Google Scholar 

  7. Huang W, Yen RT, McLaurine M, Bledsoe G. Morphometry of the human pulmonary vasculature. J Appl Physiol. 1996;81(5):2123–33.

    Article  Google Scholar 

  8. Jimenez-Carretero D, Bermejo-Peláez S, Nardelli P, Fraga P, Fraile E, Estépar RS, Ledesma-Carbayo MJ. A graph-cut approach for pulmonary artery-vein segmentation in noncontrast CT images. Med Image Anal. 2019;52:144–59.

    Article  Google Scholar 

  9. Gao Z, Grout RW, Holtze C, Hoffman EA, Saha P. A new paradigm of interactive artery/vein separation in noncontrast pulmonary CT imaging using multiscale topomorphologic opening. IEEE Trans Biomed Eng. 2012;59(11):3016–27. https://doi.org/10.1109/TBME.2012.2212894.

    Article  Google Scholar 

  10. Charbonnier JP, Brink M, Ciompi F, Scholten ET, Schaefer-Prokop CM, Van Rikxoort EM. Automatic pulmonary artery-vein separation and classification in computed tomography using tree partitioning and peripheral vessel matching. IEEE Trans Med Imaging. 2016;35(3):882–92.

    Article  Google Scholar 

  11. Zhang Z, Li Y, Shin BS. Robust color medical image segmentation on unseen domain by randomized illumination enhancement. Comput Biol Med. 2022;145:105427.

    Article  Google Scholar 

  12. Nardelli P, Jimenez-Carretero D, Bermejo-Pelaez D, Ledesma-Carbayo MJ, Rahaghi Farbod N, San Jose Estepar R. Deep-learning strategy for pulmonary artery-vein classification of non-contrast CT images. In: 2017 IEEE 14th international symposium on biomedical imaging (ISBI 2017). 2017. p. 384–87.

  13. Pu J, Leader JK, Sechrist J, Beeche CA, Singh JP, Ocak IK. Automated identification of pulmonary arteries and veins depicted in non-contrast chest CT scans. Med Image Anal. 2022;77:102367.

    Article  Google Scholar 

  14. Moses D, Sammut C, Zrimec T. Automatic segmentation and analysis of the main pulmonary artery on standard post-contrast CT studies using iterative erosion and dilation. Int J Comput Assist Radiol Surg. 2016;11(3):381–95.

    Article  Google Scholar 

  15. Nardelli P, Jimenez-Carretero D, Bermejo-Pelaez D, Washko GR, Rahaghi FN, Ledesma-Carbayo MJ, Estepar RSJ. Pulmonary artery-vein classification in CT images using deep learning. IEEE Trans Med Imaging. 2018;37(11):2428–40.

    Article  Google Scholar 

  16. Bozkurt F, Köse C, Sari A. Segmentation of carotid arteries in CTA images using region-based active contours and classification. In: 2017 international artificial intelligence and data processing symposium (IDAP). Malatya, Turkey, 2017, pp. 1–8. https://doi.org/10.1109/IDAP.2017.8090261

  17. Du H, Shao K, Bao F, Zhang Y, Gao C, Wu W, Zhang C. Automated coronary artery tree segmentation in coronary CTA using a multiobjective clustering and toroidal model-guided tracking method. Comput Methods Prog Biomed. 2021;199:105908.

    Article  Google Scholar 

  18. Nee LH, et al. White blood cell segmentation for acute leukemia bone marrow images. In: International conference on biomedical engineering. 2012.

  19. Tan C, et al. Vessel enhancement and segmentation of 4D CT lung image using stick tensor voting. Sens Imaging. 2016;17(1):1–16.

    Google Scholar 

  20. Helmberger M, Urschler M, Pienn M, et al. Pulmonary vascular tree segmentation from contrast-enhanced CT images. arXiv. 2013. https://doi.org/10.48550/arXiv.1304.7140.

  21. Hu S, Hoffman EA, Reinhardt JM. Automatic lung segmentation for accurate quantitation of volumetric X-ray CT images. IEEE Trans Med Imaging. 2001;20(6):490–8.

    Article  Google Scholar 

  22. Dharmalingham V, Kumar D. A model based segmentation approach for lung segmentation from chest computer tomography images. Multimed Tools Appl. 2020;79(15–16):10003–28.

    Article  Google Scholar 

  23. Naseri Samaghcheh Z, et al. A new model-based framework for lung tissue segmentation in three-dimensional thoracic CT images. Signal Image Video Process. 2018;12(2):339–46.

    Article  Google Scholar 

  24. Carvalho LE, et al. 3D segmentation algorithms for computerized tomographic imaging: a systematic literature review. J Digit Imaging. 2018;31(6):799–850.

    Article  Google Scholar 

  25. Wang Q, et al. HOSVD-based 3D active appearance model: segmentation of lung fields in CT images. J Med Syst. 2016;40(7):176.

    Article  Google Scholar 

  26. Salama WM, et al. Lung images segmentation and classification based on deep learning: a new automated CNN approach. J Phys Conf Ser. 2021;2128(1):12011.

    Article  Google Scholar 

  27. Geng H, Tan W-J, Yang J-Z, Bian Z-J, Zhao D-Z. Pulmonary tissue segmentation and quantitative function analysis based on CT image. Xiao Wei Xing Ji Suan Ji Xi Tong. 2016;37(03):581–7.

    Google Scholar 

  28. Buelow T, Wiemker R, Blaffert T, Lorenz C, Renisch S. Automatic extraction of the pulmonary artery tree from multi-slice CT data. In: SPIE medical imaging. 2005. p. 730–740

  29. Otsu N. A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern. 1979;9:62–6.

    Article  Google Scholar 

  30. Tan W, Li X, Zhou Q, Liu P, Yang J. Pulmonary image anatomical structure segmentation dataset and applications. Zhongguo tu xiang tu xing xue bao. 2021;26(9):2111–20.

    Google Scholar 

  31. Boykov YY. Interactive graph cuts for optimal boundary & region segmentation of objects in n-d images. In: Proceedings of the eighth IEEE international conference on computer vision, vol. 7. IEEE Computer Society; 2001. p. 105–112.

  32. Boykov Y, Veksler O. Graph cuts in vision and graphics: theories and applications. In: Handbook of mathematical models in computer vision. Springer; 2006. p. 79–96.

  33. Goodfellow I, Bengio Y, Courville A. Deep learning, vol. 1. Cambridge: MIT Press; 2016. p. 326–66.

    Google Scholar 

  34. Gu J, Wang Z, Kuen J, Ma L, Shahroudy A, Shuai B, Liu T, Wang X, Wang L, Wang G, Cai J. Recent advances in convolutional neural networks. arXiv Preprint. 2015. arXiv:1512.07108.

  35. Zunair H, Ben Hamza A. Sharp U-Net: depthwise convolutional network for biomedical image segmentation. Comput Biol Med. 2021;136:104699.

    Article  Google Scholar 

  36. Wu R, Xin Y, Qian J, Dong Y. A multi-scale interactive U-Net for pulmonary vessel segmentation method based on transfer learning. Biomed Signal Process Control. 2023;80:104407.

    Article  Google Scholar 

  37. Chen C, Zhang C, Wang J, Li D, Li Y, Hong J. Semantic segmentation of mechanical assembly using selective kernel convolution UNet with fully connected conditional random field. Measurement. 2023;209:112499.

    Article  Google Scholar 

  38. Zhou Y, Kong Q, Zhu Y, Su Z. MCFA-UNet: multiscale cascaded feature attention U-Net for liver segmentation. IRBM. 2023;44(4):100789.

    Article  Google Scholar 

  39. Han J, Wang Y, Gong H. Fundus retinal vessels image segmentation method based on improved U-Net. IRBM. 2022;43(6):628–39.

    Article  Google Scholar 

Download references

Acknowledgements

This work is supported by the National Natural Science Foundation of China (61971118), Fundamental Research Funds for the Central Universities (N2216014), Science and Technology Plan of Liaoning Province (2021JH1/10400051).

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Authors and Affiliations

  1. Key Laboratory of Intelligent Computing in Medical Image, Ministry of Education, Northeastern University, Shenyang, 110189, China

    Qinghua Zhou, Wenjun Tan, Qingya Li, Baoting Li, Luyu Zhou, Xin Liu, Jinzhu Yang & Dazhe Zhao

  2. College of Computer Science and Engineering, Northeastern University, Shenyang, 110189, China

    Qinghua Zhou, Wenjun Tan, Qingya Li, Baoting Li, Luyu Zhou, Xin Liu, Jinzhu Yang & Dazhe Zhao

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  1. Qinghua Zhou
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  2. Wenjun Tan
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  3. Qingya Li
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  5. Luyu Zhou
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  6. Xin Liu
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Correspondence to Wenjun Tan or Dazhe Zhao.

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Zhou, Q., Tan, W., Li, Q. et al. A new segment method for pulmonary artery and vein. Health Inf Sci Syst 11, 47 (2023). https://doi.org/10.1007/s13755-023-00245-8

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