Zusammenfassung
Histological evaluation of tissue samples is a typical approach to identify colorectal cancer metastases in the peritoneum. For immediate assessment, reliable and real-time in-vivo imaging would be required. For example, intraoperative confocal laser microscopy has been shown to be suitable for distinguishing organs and also malignant and benign tissue. So far, the analysis is done by human experts. We investigate the feasibility of automatic colon cancer classification from confocal laser microscopy images using deep learning models. We overcome very small dataset sizes through transfer learning with state-of-the-art architectures. We achieve an accuracy of 89:1% for cancer detection in the peritoneum which indicates viability as an intraoperative decision support system.
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Literatur
Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA: Cancer J Clin. 2015;65(2):87-108.
Franko J, Shi Q, Goldman CD, et al. Treatment of colorectal peritoneal carcinomatosis with systemic chemotherapy: a pooled analysis of north central cancer treatment group phase III trials N9741 and N9841. J Clin Oncol. 2012;30(3):263.
Ellebrecht DB, Kuempers C, Horn M, et al. Confocal laser microscopy as novel approach for real-time and in-vivo tissue examination during minimal-invasive surgery in colon cancer. Surg Endosc. 2018; p. 1-7.
Litjens G, Kooi T, Bejnordi BE, et al. A survey on deep learning in medical image analysis. Med Image Anal. 2017;42:60-88.
Esteva A, Kuprel B, Novoa RA, et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature. 2017;542(7639):115.
Hoo-Chang S, Roth HR, Gao M, et al. Deep convolutional neural networks for computer-aided detection: CNN architectures, dataset characteristics and transfer learning. IEEE Trans Med Imaging. 2016;35(5):1285.
Gessert N, Lutz M, Heyder M, et al. Automatic plaque detection in IVOCT pullbacks using convolutional neural networks. IEEE Trans Med Imaging. 2018; p. 1-9.
Huang G, Liu Z, Weinberger KQ, et al. Densely connected convolutional networks. Proc CVPR. 2017;.
Hu J, Shen L, Sun G. Squeeze-and-excitation networks. Proc CVPR. 2018;.
Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks. Proc CVPR. 2017; p. 5987-5995.
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© 2019 Springer Fachmedien Wiesbaden GmbH, ein Teil von Springer Nature
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Gessert, N., Wittig, L., Drömann, D., Keck, T., Schlaefer, A., Ellebrecht, D.B. (2019). Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks. In: Handels, H., Deserno, T., Maier, A., Maier-Hein, K., Palm, C., Tolxdorff, T. (eds) Bildverarbeitung für die Medizin 2019. Informatik aktuell. Springer Vieweg, Wiesbaden. https://doi.org/10.1007/978-3-658-25326-4_72
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DOI: https://doi.org/10.1007/978-3-658-25326-4_72
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