Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks
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.
Unable to display preview. Download preview PDF.
- 1.Torre LA, Bray F, Siegel RL, et al. Global cancer statistics, 2012. CA: Cancer J Clin. 2015;65(2):87-108.Google Scholar
- 3.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.Google Scholar
- 6.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.Google Scholar
- 7.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.Google Scholar
- 8.Huang G, Liu Z, Weinberger KQ, et al. Densely connected convolutional networks. Proc CVPR. 2017;.Google Scholar
- 9.Hu J, Shen L, Sun G. Squeeze-and-excitation networks. Proc CVPR. 2018;.Google Scholar
- 10.Xie S, Girshick R, Dollár P, et al. Aggregated residual transformations for deep neural networks. Proc CVPR. 2017; p. 5987-5995.Google Scholar