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Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2020 (MICCAI 2020)


Intra-operative (this work was partially supported by Disruptive Technologies Innovation Fund, Ireland, project code DTIF2018 240 CA) identification of malignant versus benign or healthy tissue is a major challenge in fluorescence guided cancer surgery. We propose a perfusion quantification method for computer-aided interpretation of subtle differences in dynamic perfusion patterns which can be used to distinguish between normal tissue and benign or malignant tumors intra-operatively by using multispectral endoscopic videos. The method exploits the fact that vasculature arising from cancer angiogenesis gives tumors differing perfusion patterns from the surrounding normal tissues. Experimental evaluation of our method on a cohort of colorectal cancer surgery endoscopic videos suggests that it discriminates between healthy, cancerous and benign tissues with 95% accuracy.

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  1. 1.

    Perfusion is the passage of fluid through the circulatory or lymphatic system to a capillary bed in tissue.


  1. Benson, R.C., Kues, H.A.: Fluorescence properties of indocyanine green as related to angiography. Phys. Med. Biol. 23(1), 159–163 (1978).

    Article  Google Scholar 

  2. Boni, L., David, G., Dionigi, G., Rausei, S., Cassinotti, E., Fingerhut, A.: Indocyanine green-enhanced fluorescence to assess bowel perfusion during laparoscopic colorectal resection. Surg. Endosc. 30(7), 2736–2742 (2015).

    Article  Google Scholar 

  3. Choi, M., Choi, K., et al.: Dynamic fluorescence imaging for multiparametric measurement of tumor vasculature. J. Biomed. Optics 16(4), 046008 (2011).

    Article  Google Scholar 

  4. De Palma, M., Biziato, D., Petrova, T.V.: Microenvironmental regulation of tumour angiogenesis. Nat. Rev. Cancer 17(8), 457 (2017).

    Article  Google Scholar 

  5. Diana, M., et al.: Enhanced-reality video fluorescence: a real-time assessment of intestinal viability. Ann. Surg. 259(4), 700–707 (2014).

    Article  Google Scholar 

  6. Gurfinkel, M., et al.: Pharmacokinetics of icg and hpph-car for the detection of normal and tumor tissue using fluorescence, near-infrared reflectance imaging: a case study. Photochem. Photobiol. 72(1), 94–102 (2000).

    Article  Google Scholar 

  7. Holt, D., et al.: Intraoperative near-infrared imaging can distinguish cancer from normal tissue but not inflammation. PLOS ONE. 9(7), e103342 (2014).

    Article  Google Scholar 

  8. Huh, Y.J., et al.: Efficacy of assessing intraoperative bowel perfusion with near-infrared camera in laparoscopic gastric cancer surgery. J. Laparoendosc. Adv. Surg. Tech. 29(4), 476–483 (2019).

    Article  Google Scholar 

  9. Jayender, J., et al.: Statistical learning algorithm for in situ and invasive breast carcinoma segmentation. Comput. Med. Imaging Graph 37(4), 281–292 (2013).

    Article  Google Scholar 

  10. Jones, E., et al.: SciPy: open source scientific tools for Python, (2001)

  11. McKinney, S.M., et al.: International evaluation of an ai system for breast cancer screening. Nat. 577(7788), 89–94 (2020).

    Article  Google Scholar 

  12. Nishida, N., et al.: Angiogenesis in cancer. Vasc. Health Risk Manage. 2(3), 213 (2006).

    Article  Google Scholar 

  13. Pedregosa, F., et al.: Scikit-learn: machine learning in Python. J. Mach. Learn. Res. 12, 2825–2830 (2011).

    Article  MathSciNet  MATH  Google Scholar 

  14. Phillips, C.L., et al.: Feedback Control Systems. Prentice Hall, 4 ed, (2000)

    Google Scholar 

  15. Schaafsma, B.E., et al.: The clinical use of indocyanine green as a near-infrared fluorescent contrast agent for image-guided oncologic surgery. J.Surg. Oncol. 104(3), 323–332 (2011).

    Article  Google Scholar 

  16. Selka, F., et al.: Fluorescence-based enhanced reality for colorectal endoscopic surgery. In: Ourselin, S., Modat, M. (eds.) WBIR 2014. LNCS, vol. 8545, pp. 114–123. Springer, Cham (2014).

    Chapter  Google Scholar 

  17. Shapcott, C.M., Rajpoot, N., Hewitt, K.: Deep learning with sampling for colon cancer histology images. Front. Bioeng. Biotech. 7, 52 (2019).

    Article  Google Scholar 

  18. Son, G.M., Kwon, M.S., Kim, Y., Kim, J., Kim, S.H., Lee, J.W.: Quantitative analysis of colon perfusion pattern using indocyanine green (ICG) angiography in laparoscopic colorectal surgery. Surg. Endosc. 33(5), 1640–1649 (2018).

    Article  Google Scholar 

  19. Veys, I., et al.: Icg-fluorescence imaging for detection of peritoneal metastases and residual tumoral scars in locally advanced ovarian cancer: a pilot study. J. Surg. Oncol. 117(2), 228–235 (2018).

    Article  Google Scholar 

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Correspondence to Sergiy Zhuk .

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Zhuk, S. et al. (2020). Perfusion Quantification from Endoscopic Videos: Learning to Read Tumor Signatures. In: Martel, A.L., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2020. MICCAI 2020. Lecture Notes in Computer Science(), vol 12263. Springer, Cham.

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  • Print ISBN: 978-3-030-59715-3

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