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Computer-assisted real-time automatic prostate segmentation during TaTME: a single-center feasibility study

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Abstract

Background

Urethral injuries (UIs) are significant complications pertaining to transanal total mesorectal excision (TaTME). It is important for surgeons to identify the prostate during TaTME to prevent UI occurrence; intraoperative image navigation could be considered useful in this regard. This study aims at developing a deep learning model for real-time automatic prostate segmentation based on intraoperative video during TaTME. The proposed model’s performance has been evaluated.

Methods

This was a single-institution retrospective feasibility study. Semantic segmentation of the prostate area was performed using a convolutional neural network (CNN)-based approach. DeepLab v3 plus was utilized as the CNN model for the semantic segmentation task. The Dice coefficient (DC), which is calculated based on the overlapping area between the ground truth and predicted area, was utilized as an evaluation metric for the proposed model.

Results

Five hundred prostate images were randomly extracted from 17 TaTME videos, and the prostate area was manually annotated on each image. Fivefold cross-validation tests were performed, and as observed, the average DC value equaled 0.71 ± 0.04, the maximum value being 0.77. Additionally, the model operated at 11 fps, which provides acceptable real-time performance.

Conclusions

To the best of the authors’ knowledge, this is the first effort toward realization of computer-assisted TaTME, and results obtained in this study suggest that the proposed deep learning model can be utilized for real-time automatic prostate segmentation. In future endeavors, the accuracy and performance of the proposed model will be improved to enable its use in practical applications, and its capability to reduce UI risks during TaTME will be verified.

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References

  1. Sylla P, Rattner DW, Delgado S, Lacy AM (2010) NOTES transanal rectal cancer resection using transanal endoscopic microsurgery and laparoscopic assistance. Surg Endosc 24:1205–1210

    Article  Google Scholar 

  2. Buchs NC, Penna M, Bloemendaal AL, Hompes R (2016) Transanal total mesorectal excision: myths and reality. World J Clin Oncol 7:337–339

    Article  Google Scholar 

  3. Monson JRT, Arsalanizadeh R (2016) Transanal total mesorectal excision (TaTME) and quality of rectal cancer surgery: do we really know? Ann Surg 266:e88–e89

    Article  Google Scholar 

  4. Deijen CL, Tsai A, Koedam TWA et al (2016) Clinical outcomes and case volume effect of transanal total mesorectal excision for rectal cancer: a systematic review. Tech Coloproctol 20:811–824

    Article  CAS  Google Scholar 

  5. Penna M, Hompes R, Arnold S et al (2016) Transanal total mesorectal excision: International registry results of the first 720 cases. Ann Surg 266:111–117

    Article  Google Scholar 

  6. Burke J, Martin-Perez B, Khan A et al (2016) Transanal total mesorectal excision for rectal cancer: early outcomes in 50 consecutive patients. Colorectal Dis 18:570–577

    Article  CAS  Google Scholar 

  7. Rouanet P, Mourregot A, Azar C et al (2013) Transanal endoscopic proctectomy: an innovative procedure for difficult resection of rectal tumors in men with narrow pelvis. Dis Colon Rectum 56:408–415

    Article  Google Scholar 

  8. Kang L, Chen W, Luo S et al (2016) Transanal total mesorectal excision for rectal cancer: a preliminary report. Surg Endosc 30:2552–2562

    Article  Google Scholar 

  9. Schirnhofer J, Brunner E, Mittermair C et al (2014) Technical issues in transanal minimal invasive surgery: total mesorectal excisions (TAMIS-TME). Eur Surg 46:S58

    Google Scholar 

  10. Perdawood S, Thinggaard B, Bjoern M (2018) Effect of transanal total mesorectal excision for rectal cancer: comparison of short-term outcomes with laparoscopic and open surgeries. Surg Endosc 32:2312–2321

    Article  Google Scholar 

  11. Shelhamer E, Long J, Darrell T (2017) Fully convolutional networks for semantic segmentation. IEEE Trans Pattern Anal Mach Intell 39(4):640–651

    Article  Google Scholar 

  12. Ghavami N, Hu Y, Gibson E et al (2019) Automatic segmentation of prostate MRI using convolutional neural networks: investigating the impact of network architecture on the accuracy of volume measurement and MRI-ultrasound registration. Med Image Anal 58:101558

    Article  Google Scholar 

  13. Karimi D, Samei G, Kesch C et al (2018) Prostate segmentation in MRI using a convolutional neural network architecture and training strategy based on statistical shape models. Int J Comput Assist Radiol Surg 13(8):1211–1219

    Article  Google Scholar 

  14. Ma L, Guo R, Zhang G et al (2017) Automatic segmentation of the prostate on CT images using deep learning and multi-atlas fusion. Proc SPIE Int Soc Opt Eng 10133:101332O

    PubMed  PubMed Central  Google Scholar 

  15. Liu C, Gardner SJ, Wen N et al (2019) Automatic segmentation of the prostate on CT images using deep neural networks (DNN). Int J Radiat Oncol Biol Phys 104(4):924–932

    Article  Google Scholar 

  16. Wijsmuller AR, Romagnolo LGC, Agnus V et al (2018) Advances in stereotactic navigation for pelvic surgery. Surg Endosc 32(6):2713–2720

    Article  CAS  Google Scholar 

  17. Kwak JM, Romagnolo L, Wijsmuller A et al (2019) Stereotactic pelvic navigation with augmented reality for transanal total mesorectal excision. Dis Colon Rectum 62(1):123–129

    Article  Google Scholar 

  18. Chen LC, Zhu Y, Papandreou G et al (2018) Encoder-decoder with atrous separable convolution for semantic image segmentation. arXiv:1802.02611v3 [cs.CV] 22 Aug 2018.

  19. Russakovsky O, Deng J, Su H et al (2015) Imagenet large scale visual recognition challenge. Int J Comput Vis 115:211–252

    Article  Google Scholar 

  20. Atallah S, Albert M, Monson JR (2016) Critical concepts and important anatomic landmarks encountered during transanal total mesorectal excision (taTME): toward the mastery of a new operation for rectal cancer surgery. Tech Coloproctol 20:483–494. https://doi.org/10.1007/s10151-016-1475-x

    Article  CAS  PubMed  Google Scholar 

  21. Knol J, Chadi SA (2016) Transanal total mesorectal excision: technical aspects of approaching the mesorectal plane from below. Minim Invasive Ther Allied Technol 25:257–270. https://doi.org/10.1080/13645706.2016.1206572

    Article  PubMed  Google Scholar 

  22. Atallah S, Mabardy A, Volpato AP et al (2017) Surgery beyond the visible light spectrum: theoretical and applied methods for localization of the male urethra during transanal total mesorectal excision. Tech Coloproctol 21:413–424

    Article  CAS  Google Scholar 

  23. Atallah S, Martin-Perez B, Drake J et al (2015) The use of a lighted stent as a method for identifying the urethra in male patients undergoing transanal total mesorectal excision: a video demonstration. Tech Coloproctol 19:375. https://doi.org/10.1007/s10151-015-1297-2

    Article  CAS  PubMed  Google Scholar 

  24. Barnes TG, Penna M, Hompes R, Cunningham C (2017) Fluorescence to highlight the urethra: a human cadaveric study. Tech Coloproctol 21:439–444. https://doi.org/10.1007/s10151-017-1615-y

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  25. Nitta T, Tanaka K, Kataoka J et al (2019) Novel technique with the IRIS U kit to prevent urethral injury in patients undergoing transanal total mesorectal excision. Ann Med Surg 46:1–3

    Article  Google Scholar 

  26. Koedam TWA, Veltcamp Helbach M, van de Ven PM et al (2018) Transanal total mesorectal excision for rectal cancer: evaluation of the learning curve. Tech Coloproctol 22:279–287. https://doi.org/10.1007/s10151-018-1771-8

    Article  CAS  PubMed  Google Scholar 

  27. Lee L, Kelly J, Nassif GJ et al (2020) Defining the learning curve for transanal total mesorectal excision for rectal adenocarcinoma. Surg Endosc 34:1534–1542. https://doi.org/10.1007/s00464-018-6360-4

    Article  PubMed  Google Scholar 

  28. Veltcamp Helbach M, van Oostendorp SE, Koedam TWA et al (2020) Structured training pathway and proctoring; multicenter results of the implementation of transanal total mesorectal excision (TaTME) in the Netherlands. Surg Endosc 34:192–201. https://doi.org/10.1007/s00464-019-06750-w

    Article  CAS  PubMed  Google Scholar 

  29. van Oostendorp SE, Belgers HJ, Bootsma BT et al (2020) Locoregional recurrences after transanal total mesorectal excision of rectal cancer during implementation. Br J Surg. https://doi.org/10.1002/bjs.11525

    Article  PubMed  PubMed Central  Google Scholar 

  30. Allan M, Shvets A, Kurmann T et al (2019) 2017 robotic instrument segmentation challenge. arXiv:1902.06426v2 [cs.CV] 21 Feb 2019

  31. Wang Y, Zhou Y, Shen W et al (2019) Abdominal multi-organ segmentation with organ-attention networks and statistical fusion. Med Image Anal 55:88–102

    Article  Google Scholar 

Download references

Acknowledgements

This work was supported by JSPS KAKENHI Grant Number JP18K16378.

Funding

This work was supported by Japan Society for the Promotion of Science (JSPS KAKENHI) Grant Number JP18K16378.

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Authors and Affiliations

  1. Surgical Device Innovation Office, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan

    Daichi Kitaguchi, Nobuyoshi Takeshita, Hiroki Matsuzaki, Hiro Hasegawa & Masaaki Ito

  2. Department of Colorectal Surgery, National Cancer Center Hospital East, 6-5-1 Kashiwanoha, Kashiwa, Chiba, 277-8577, Japan

    Daichi Kitaguchi, Nobuyoshi Takeshita, Hiro Hasegawa, Ryoya Honda, Koichi Teramura & Masaaki Ito

  3. Department of Gastrointestinal and Hepato-Biliary-Pancreatic Surgery, Faculty of Medicine, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8575, Japan

    Daichi Kitaguchi & Tatsuya Oda

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  1. Daichi Kitaguchi
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Correspondence to Nobuyoshi Takeshita or Masaaki Ito.

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Disclosure

Daichi Kitaguchi, Nobuyoshi Takeshita, Hiroki Matsuzaki, Hiro Hasegawa, Ryoya Honda MD, Koichi Teramura, Tatsuya Oda, and Masaaki Ito have no conflicts of interest or financial ties to disclose.

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Kitaguchi, D., Takeshita, N., Matsuzaki, H. et al. Computer-assisted real-time automatic prostate segmentation during TaTME: a single-center feasibility study. Surg Endosc 35, 2493–2499 (2021). https://doi.org/10.1007/s00464-020-07659-5

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  • DOI: https://doi.org/10.1007/s00464-020-07659-5

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