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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The data that support the findings of this study are available from the corresponding author upon reasonable request.
References
Tellapuri S, Park HS, Kalva SP. Pulmonary arteriovenous malformations. Int J Cardiovasc Imaging. 2019;35(8):1421–8.
Papagiannis J, Apostolopoulou S, Sarris G, Rammos S. Diagnosis and management of pulmonary arteriovenous malformations. Images Paediatr Cardiol. 2002;4(1):33–49.
Cummings KW, Bhalla S. Pulmonary vascular diseases. Clin Chest Med. 2015;36(2):235-248 viii.
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.
Rahaghi FN, et al. Pulmonary vascular morphology as an imaging biomarker in chronic thromboembolic pulmonary hypertension. Pulm Circ. 2016;6(1):70–81.
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.
Huang W, Yen RT, McLaurine M, Bledsoe G. Morphometry of the human pulmonary vasculature. J Appl Physiol. 1996;81(5):2123–33.
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.
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.
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.
Zhang Z, Li Y, Shin BS. Robust color medical image segmentation on unseen domain by randomized illumination enhancement. Comput Biol Med. 2022;145:105427.
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.
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.
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.
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.
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
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.
Nee LH, et al. White blood cell segmentation for acute leukemia bone marrow images. In: International conference on biomedical engineering. 2012.
Tan C, et al. Vessel enhancement and segmentation of 4D CT lung image using stick tensor voting. Sens Imaging. 2016;17(1):1–16.
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.
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.
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.
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.
Carvalho LE, et al. 3D segmentation algorithms for computerized tomographic imaging: a systematic literature review. J Digit Imaging. 2018;31(6):799–850.
Wang Q, et al. HOSVD-based 3D active appearance model: segmentation of lung fields in CT images. J Med Syst. 2016;40(7):176.
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.
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.
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
Otsu N. A threshold selection method from gray level histograms. IEEE Trans Syst Man Cybern. 1979;9:62–6.
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.
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.
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.
Goodfellow I, Bengio Y, Courville A. Deep learning, vol. 1. Cambridge: MIT Press; 2016. p. 326–66.
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.
Zunair H, Ben Hamza A. Sharp U-Net: depthwise convolutional network for biomedical image segmentation. Comput Biol Med. 2021;136:104699.
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.
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.
Zhou Y, Kong Q, Zhu Y, Su Z. MCFA-UNet: multiscale cascaded feature attention U-Net for liver segmentation. IRBM. 2023;44(4):100789.
Han J, Wang Y, Gong H. Fundus retinal vessels image segmentation method based on improved U-Net. IRBM. 2022;43(6):628–39.
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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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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DOI: https://doi.org/10.1007/s13755-023-00245-8