Abstract
Lung cancer, the second most common type of cancer worldwide, is primarily treated through surgery. During the operations, preserving pulmonary arteries and veins is a crucial problem. In recent years, 3D visualization techniques like virtual reality and 3D printing have been increasingly used in clinical practice for lung cancer surgery planning. Under the success of these techniques, automatic segmentation of pulmonary arteries and veins plays a key role. Particularly, the state-of-art approaches rely on two techniques, i.e. the deep neural networks (DNNs) or the traditional machine learning (ML) method, and both techniques have respective shortages. Basically, the ML-based methods generally demonstrate a limited performance, while the DNN-based methods lack sufficient annotation for accurate segmentation. In response to such a dilemma, this paper proposes a fusion method to combine the DNN-based and ML-based methods to segment pulmonary arteries and veins for lung cancer surgery planning. Particularly, the anatomy prior mask corresponding to pulmonary arteries and veins are identified using the marching cubes algorithm and Attention U-Net. Subsequently, an enhanced attention U-Net, is used to integrate the original CT scans with the anatomy prior mask to generate the refined segmentation results. Following this, an anatomy structure enhancement module is used to refine the segmentation further by refining disconnected vessel segments and correcting misclassified vessels based on anatomy prior masks. We experimented the proposed approach on a private dataset of 95 CT scans collected from patients after surgery, and then annotated by lung cancer experts. The results demonstrate that our approach outperforms the existing methods with an improvement of 5.1% to 16.2% in Dice score. The dataset and code have also been published [1] to facilitate further research in this field.
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References
Dataset. https://github.com/XiaoweiXu/PulmonaryVesselSegSurgicalPlanning
Charbonnier, J.P., Brink, M., Ciompi, F., Scholten, E.T., Schaefer-Prokop, C.M., Van Rikxoort, E.M. (eds.): Automatic pulmonary artery-vein separation and classification in computed tomography using tree partitioning and peripheral vessel matching, vol. 35. IEEE (2015)
Lee, T.C., Kashyap, R.L., Chu, C.N. (eds.): Building skeleton models via 3-D medial surface axis thinning algorithms, vol. 56. Elsevier (1994)
Li, C., Zheng, B., Yu, Q., Yang, B., Liang, C., Liu, Y. (eds.): Augmented reality and 3-dimensional printing technologies for guiding complex thoracoscopic surgery, vol. 112. Elsevier (2021)
Liu, X., Zhao, Y., Xuan, Y., Lan, X., Zhao, J., Lan, X., Han, B., Jiao, W.: Three-dimensional printing in the preoperative planning of thoracoscopic pulmonary segmentectomy. Translational Lung Cancer Research 8(6), 929 (2019)
Lorensen, W.E., Cline, H.E. (eds.): Marching cubes: A high resolution 3D surface construction algorithm, vol. 21. ACM New York, NY, USA (1987)
Lorensen, W.E., Cline, H.E.: Marching cubes: A high resolution 3d surface construction algorithm. In: Seminal graphics: pioneering efforts that shaped the field, pp. 347–353 (1998)
Nardelli, P., Jimenez-Carretero, D., Bermejo-Pelaez, D., Washko, G.R., Rahaghi, F.N., Ledesma-Carbayo, M.J., Estépar, R.S.J. (eds.): Pulmonary artery–vein classification in CT images using deep learning, vol. 37. IEEE (2018)
Oktay, O., Schlemper, J., Folgoc, L.L., Lee, M., Heinrich, M., Misawa, K., Mori, K., McDonagh, S., Hammerla, N.Y., Kainz, B., et al.: Attention u-net: Learning where to look for the pancreas. arXiv preprint arXiv:1804.03999 (2018)
Payer, C., Pienn, M., Bálint, Z., Shekhovtsov, A., Talakic, E., Nagy, E., Olschewski, A., Olschewski, H., Urschler, M. (eds.): Automated integer programming based separation of arteries and veins from thoracic CT images, vol. 34. Elsevier (2016)
Pu, J., Leader, J.K., Sechrist, J., Beeche, C.A., Singh, J.P., Ocak, I.K., Risbano, M.G. (eds.): Automated identification of pulmonary arteries and veins depicted in non-contrast chest CT scans, vol. 77. Elsevier (2022)
Qin, Y., Zheng, H., Gu, Y., Huang, X., Yang, J., Wang, L., Yao, F., Zhu, Y.M., Yang, G.Z. (eds.): Learning tubule-sensitive cnns for pulmonary airway and artery-vein segmentation in ct, vol. 40. IEEE (2021)
Saji, H., Okada, M., Tsuboi, M., Nakajima, R., Suzuki, K., Aokage, K., Aoki, T., Okami, J., Yoshino, I., Ito, H., et al. (eds.): Segmentectomy versus lobectomy in small-sized peripheral non-small-cell lung cancer (JCOG0802/WJOG4607L): a multicentre, open-label, phase 3, randomised, controlled, non-inferiority trial, vol. 399. Elsevier (2022)
Sung, H., Ferlay, J., Siegel, R.L., Laversanne, M., Soerjomataram, I., Jemal, A., Bray, F. (eds.): Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, vol. 71. Wiley Online Library (2021)
Suzuki, H., Kawata, Y., Aokage, K., Matsumoto, Y., Sugiura, T., Tanabe, N., Nakano, Y., Tsuchida, T., Kusumoto, M., Marumo, K., Kaneko, M., Niki, N.: Aorta and main pulmonary artery segmentation using stacked u-net and localization on non-contrast-enhanced computed tomography images. MEDICAL PHYSICS (2023)
Wu, Y., Qi, S., Wang, M., Zhao, S., Pang, H., Xu, J., Bai, L., Ren, H.: Transformer-based 3d u-net for pulmonary vessel segmentation and artery-vein separation from ct images. Medical & Biological Engineering & Computing 61(10), 2649–2663 (2023)
Xu, X., Jia, Q., Yuan, H., Qiu, H., Dong, Y., Xie, W., Yao, Z., Zhang, J., Nie, Z., Li, X., et al.: A clinically applicable ai system for diagnosis of congenital heart diseases based on computed tomography images. Med. Image Anal. 90, 102953 (2023)
Xu, X., Wang, T., Shi, Y., Yuan, H., Jia, Q., Huang, M., Zhuang, J.: Whole heart and great vessel segmentation in congenital heart disease using deep neural networks and graph matching. In: MICCAI, Shenzhen, China, October 13–17, 2019, Proceedings, Part II 22. pp. 477–485. Springer (2019)
Xu, X., Wang, T., Zhuang, J., Yuan, H., Huang, M., Cen, J., Jia, Q., Dong, Y., Shi, Y.: Imagechd: A 3d computed tomography image dataset for classification of congenital heart disease. In: MICCAI, Lima, Peru, October 4–8, 2020, Proceedings, Part IV 23. pp. 77–87. Springer (2020)
Zulfiqar, M., Stanuch, M., Wodzinski, M., Skalski, A.: Dru-net: Pulmonary artery segmentation via dense residual u-network with hybrid loss function. Sensors 23(12), 5427 (2023)
Acknowledgement
This work was supported by the National Natural Science Foundation of China (No. 62276071), Guangdong Special Support Program-Science and Technology Innovation Talent Project (No. 0620220211), the Science and Technology Planning Project of Guangdong Province, China (No. 2019B020230003), Guangdong Peak Project (No. DFJH201802), Guangzhou Science and Technology Planning Project (No. 202206010049), Guangdong Basic and Applied Basic Research Foundation (No. 2022A1515010157).
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Cheng, H., Zheng, L., Yan, Z., Zhang, H., Meng, B., Xu, X. (2025). Fusion of Machine Learning and Deep Neural Networks for Pulmonary Arteries and Veins Segmentation in Lung Cancer Surgery Planning. In: Antonacopoulos, A., Chaudhuri, S., Chellappa, R., Liu, CL., Bhattacharya, S., Pal, U. (eds) Pattern Recognition. ICPR 2024. Lecture Notes in Computer Science, vol 15312. Springer, Cham. https://doi.org/10.1007/978-3-031-78198-8_28
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