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Path planning for percutaneous lung biopsy based on the loose-Pareto and adaptive heptagonal optimization method

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Abstract

Lung cancer has is highly prevalent worldwide and is the leading cause of cancer-related deaths. In the clinic, a biopsy sample of the lesion is taken to determine whether a lung mass is benign or malignant. CT-guided percutaneous lung biopsy is a minimally invasive intervention and is commonly used to diagnose lung cancer. Path planning before surgery plays a crucial role in percutaneous lung biopsy. Traditionally, path planning for lung biopsy is performed manually by physicians based on CT images of the patient, which demands knowledge and extensive clinical experience of the operating physicians. In this work, a computer-assisted path planning system for percutaneous lung biopsy is proposed based on clinical objectives. Five constraints are presented to remove unqualified skin entry points and determine a feasible entry region based on clinical criteria. Inspired by the Pareto principle and the concept of geometric weighting, the loose-Pareto and adaptive heptagonal optimization (LPHO) method is introduced to plan the optimal puncture path. CT images of 29 patients were collected from Zigong First People’s Hospital. Retrospective experiments and test experiments were conducted to evaluate the effectiveness of the algorithm. The planning paths obtained using the proposed method were clinically feasible for 89.7% of patients, demonstrating the applicability and robustness of the system in surgical path planning for lung biopsy.

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References

  1. Liao F, Liang M, Li Z, Hu X, Song S (2019) Evaluate the malignancy of pulmonary nodules using the 3-d deep leaky noisy-or network. IEEE Trans Neural Netw Learn Syst 30(11):3484–3495. https://doi.org/10.1109/TNNLS.2019.2892409

    Article  PubMed  Google Scholar 

  2. Mukherjee J, Kar M, Chakrabarti A, Das S (2020) A soft-computing based approach towards automatic detection of pulmonary nodule. Biocybern Biomed Eng 40(3):1036–1051. https://doi.org/10.1016/j.bbe.2020.03.006

    Article  Google Scholar 

  3. Shi Z, Hu Q, Yue Y, Wang Z, AL-Othmani OMS, Li H (2020) Automatic nodule segmentation method for CT images using aggregation-U-Net generative adversarial networks. Sens Imaging 21(1):1–16. https://doi.org/10.1007/s11220-020-00304-4

    Article  Google Scholar 

  4. Hesamian MH, Jia W, He X, Wang Q, Kennedy PJ (2021) Synthetic CT images for semi-sequential detection and segmentation of lung nodules. Appl Intell 51(3):1616–1628. https://doi.org/10.1007/s10489-020-01914-x

    Article  Google Scholar 

  5. Sung H, Ferlay J, Siegel RL, Laversanne M, Soerjomataram I, Jemal A, Bray F (2021) Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA: Cancer J Clin 71(3):209–249. https://doi.org/10.3322/caac.21660

    Article  PubMed  Google Scholar 

  6. Liu Z, Li Z, Zhang Y, Zhou T, Zhang J, You W, Pan K, Li W (2021) Interpretation on the report of Global Cancer Statistics 2020. J Multidiscip Cancer Manag 7(2):1–13. https://d.wanfangdata.com.cn/periodical/zlzhzldzzz202102002. Accessed 30 Apr 2021.

    Google Scholar 

  7. Siegel RL, Miller KD, Fuchs HE, Jemal A (2022) Cancer statistics, 2022. CA: Cancer J Clin. https://doi.org/10.3322/caac.21708

  8. Yuan D, Du W, Duan X, Wang J, Ma Y, Zhang H (2017) Detection of slices including a ground-glass opacity nodule in CT volume data with semi-supervised learning. In 2017 18th IEEE/ACIS International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing (SNPD) (pp. 557–561). IEEE. https://doi.org/10.1109/SNPD.2017.8022778

  9. Turner MC, Andersen ZJ, Baccarelli A, Diver WR, Gapstur SM, Pope III CA, Prada D, Samet J, Thurston G, Cohen A (2020) Outdoor air pollution and cancer: an overview of the current evidence and public health recommendations. CA: Cancer J Clin 70(6):460–479. https://doi.org/10.3322/caac.21632.

  10. Mu L, Liu L, Niu R, Zhao B, Shi J, Li Y, Swanson M, Scheider W, Su J, Chang SC, Yu S, Zhang ZF (2013) Indoor air pollution and risk of lung cancer among Chinese female non-smokers. Cancer Causes Control 24(3):439–450. https://doi.org/10.1007/s10552-012-0130-8

    Article  PubMed  PubMed Central  Google Scholar 

  11. Chen S, Han Y, Lin J, Zhao X, Kong P (2020) Pulmonary nodule detection on chest radiographs using balanced convolutional neural network and classic candidate detection. Artif Intell Med 107:101881. https://doi.org/10.1016/j.artmed.2020.101881

    Article  PubMed  Google Scholar 

  12. Zhang G, Jiang S, Yang Z, Gong L, Ma X, Zhou Z, Bao C, Liu Q (2018) Automatic nodule detection for lung cancer in CT images: a review. Comput Biol Med 103:287–300. https://doi.org/10.1016/j.compbiomed.2018.10.033

    Article  PubMed  Google Scholar 

  13. Akter O, Moni MA, Islam MM, Quinn JM, Kamal AHM (2021) Lung cancer detection using enhanced segmentation accuracy. Appl Intell 51(6):3391–3404. https://doi.org/10.1007/s10489-020-02046-y

    Article  Google Scholar 

  14. Scorza D, El Hadji S, Cortés C, Bertelsen Á, Cardinale F, Baselli G, Essert C, De Momi E (2021) Surgical planning assistance in keyhole and percutaneous surgery: a systematic review. Med Image Anal 67:101820. https://doi.org/10.1016/j.media.2020.101820

    Article  PubMed  Google Scholar 

  15. Baegert C, Villard C, Schreck P, Soler L (2007) Multi-criteria trajectory planning for hepatic radiofrequency ablation. In International conference on medical image computing and computer-assisted intervention (pp. 676–684). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-75759-7_82

  16. Li J, Xu Y, Shen N, Feng L, Ran Z, Deng Z (2020) A practical pretreatment planning method of multiple puncturing for thermal ablation surgery. Biocybern Biomed Eng 40(4):1469–1485. https://doi.org/10.1016/j.bbe.2020.08.004

    Article  Google Scholar 

  17. Bao N, Chen Y, Liu Y, Chakraborty C (2022a) Multi-objective path planning for lung biopsy surgery. Multimed Tools Appl 1–18. https://doi.org/10.1007/s11042-021-11476-w

  18. Seitel A, Engel M, Sommer CM, Radeleff BA, Essert-Villard C, Baegert C, Fangerau M, Fritzsche KH, Yung K, Meinzer HP, Maier-Hein L (2011) Computer-assisted trajectory planning for percutaneous needle insertions. Med Phys 38(6Part1):3246–3259. https://doi.org/10.1118/1.3590374

    Article  PubMed  Google Scholar 

  19. Ren H, Campos-Nanez E, Yaniv Z, Banovac F, Abeledo H, Hata N, Cleary K (2014) Treatment planning and image guidance for radiofrequency ablation of large tumors. IEEE J Biomed Health Inform 18(3):920–928. https://doi.org/10.1109/JBHI.2013.2287202

    Article  PubMed  Google Scholar 

  20. Chen R, Jiang TA, Lu F, Wang K, Kong D (2017) Semiautomatic radiofrequency ablation planning based on constrained clustering process for hepatic tumors. IEEE Trans Biomed Eng 65(3):645–657. https://doi.org/10.1109/TBME.2017.2712161

    Article  PubMed  Google Scholar 

  21. Liang L, Cool D, Kakani N, Wang G, Ding H, Fenster A (2019) Automatic radiofrequency ablation planning for liver tumors with multiple constraints based on set covering. IEEE Trans Med Imaging 39(5):1459–1471. https://doi.org/10.1109/TMI.2019.2950947

    Article  PubMed  Google Scholar 

  22. Liang L, Cool D, Kakani N, Wang G, Ding H, Fenster A (2019b) Development of a multi-objective optimized planning method for microwave liver tumor ablation. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 110–118). Springer, Cham. https://doi.org/10.1007/978-3-030-32254-0_13

  23. Bao N, Jia J, Chen Y, Li Z, Song C, Zhao R (2020b) Surgery path planning for lung biopsy based on pareto optimization. In 2020 International Conference on Artificial Intelligence and Communication Technology (AICT 2020). https://kns.cnki.net/KCMS/detail/detail.aspx?dbcode=IPFD&filename=JKDZ202003001054. Accessed 28 Mar 2020

  24. Zhuang J (2017) Research on extraction of thoracic anatomy and path planning based on lung puncture surgical navigation. Northeastern University. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CMFD201901&filename=1018076043.nh.  Accessed 1 Dec 2016.

  25. Sun F, Pei H, Yang Y, Fan Q, Li X (2022) A method of lung puncture path planning based on multi-level constraint. J Biomed Eng 39(3):462–470. https://kns.cnki.net/kcms/detail/detail.aspx?dbcode=CJFD&dbname=CJFDLAST2022&filename=SWGC202203004&uniplatform=NZKPT&v=I423DRUmglNVSYcpFcM0eAXQR65a28YseooP8cGV9seASpyz5LMXFOI6kApyoLx3. Accessed 6 Jun 2022.

  26. Bian Z, Tan W, Liu J, Zhao D (2018) Review of anatomic segmentation methods in thoracic CT images. J Image Graphics 23(10):1450–2147. https://doi.org/10.3969/j.issn.1001-3695.2016.07.002

  27. Ginneken BV, Baggerman W, Rikxoort EMV (2008) Robust segmentation and anatomical labeling of the airway tree from thoracic CT scans. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 219–226). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85988-8_27

  28. Van Rikxoort EM, Van Ginneken B (2013) Automated segmentation of pulmonary structures in thoracic computed tomography scans: a review. Phys Med Biol 58(17):R187. https://doi.org/10.1088/0031-9155/58/17/R187

    Article  PubMed  Google Scholar 

  29. Geng H, Tan W, Yang J, Cao P, Zhao D (2016) Survey of lung tissues segmentation based on CT image. Appl Res Comput 33(7):1929–1935. https://doi.org/10.3969/j.issn.1001-3695.2016.07.002

    Article  Google Scholar 

  30. Qian YJ (2017) Liver vessel segmentation algorithm based on fuzzy enhance and Hessian matrix. In 2nd International Conference on Biomedical and Biological Engineering 2017 (BBE 2017) (pp. 540–545). Atlantis Press. https://doi.org/10.2991/bbe-17.2017.86

  31. Hofmanninger J, Prayer F, Pan J, Röhrich S, Prosch H, Langs G (2020) Automatic lung segmentation in routine imaging is primarily a data diversity problem, not a methodology problem. Eur Radiol Exp 4(1):1–13. https://doi.org/10.1186/s41747-020-00173-2

    Article  Google Scholar 

  32. Tan X, Jia Z, Wu D (1992) The use of ultrasonically guided puncture with different type of needle in diagnosing cancer. Chin J Ultrasound Med (01):10–12

  33. Li B, Cheng ZQ, Dang G, Jin SY (2010) Survey on normal estimation for 3 D point clouds. Comput Eng Appl 46(23):1–7. https://doi.org/10.3778/j.issn.1002-8331.2010.23.001

    Article  Google Scholar 

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Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 61571314]; the Zigong Key Science and Technology Plan Task [2020yxy02]; and the Sichuan University-Yibin City Strategic Cooperation Special Fund Project [2020CDYB-27].

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Authors and Affiliations

  1. College of Biomedical Engineering, Sichuan University, Chengdu, 610065, China

    Qi Liu, Geyi Zhou, Ling He & Jing Zhang

  2. Department of Radiology, Zigong First People’s Hospital, Zigong, 643000, China

    Jianquan Zhong & Ling Tang

  3. Beijing Institute of Remote Sensing Information, Beijing, 100011, China

    Yao Lu

  4. School of Nursing, The Hong Kong Polytechnic University, Hong Kong, 999077, China

    Jing Qin

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  1. Qi Liu
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Contributions

Conceptualization: Qi Liu, Geyi Zhou; methodology: Qi Liu, Geyi Zhou; formal analysis and investigation: Qi Liu; writing—original draft: Geyi Zhou; data curation: Jianquan Zhong, Ling Tang, Yao Lu; validation: Jianquan Zhong, Ling Tang; writing—review and editing: Jing Qin, Ling He; funding acquisition and supervision: Jing Zhang.

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Correspondence to Jing Zhang.

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Liu, Q., Zhou, G., Zhong, J. et al. Path planning for percutaneous lung biopsy based on the loose-Pareto and adaptive heptagonal optimization method. Med Biol Eng Comput 61, 1449–1472 (2023). https://doi.org/10.1007/s11517-022-02754-2

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