Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-site Robustness Assessment

Marc Aubreville, Miguel Goncalves, Christian Knipfer, Christian Knipfer, Nicolai Oetter, Nicolai Oetter, Tobias Würfl, Helmut Neumann, Florian Stelzle, Florian Stelzle, Christopher Bohr, Andreas Maier

2018

Abstract

Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accuracy of 89.45% and an area-under-the- curve (AUC) value of 0.955, when applied on the vocal cords data. Compared to the state of the art, we achieve very similar results, yet with an algorithm that was trained on a completely disjunct data set. Concatenating both data sets yielded further improvements in cross-validation with an accuracy of 90.81% and AUC of 0.970. In this study, for the first time to our knowledge, a deep learning mechanism for the identification of oral carcinomas using CLE Images could be applied to other disciplines in the area of head and neck. This study shows the prospect of the algorithmic approach to generalize well on other malignant entities of the head and neck, regardless of the anatomical location and furthermore in an examiner-independent manner.

Paper Citation

in Harvard Style

Aubreville M., Goncalves M., Knipfer C., Oetter N., Würfl T., Neumann H., Stelzle F., Bohr C. and Maier A. (2018). Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-site Robustness Assessment. In Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 2: BIOIMAGING; ISBN 978-989-758-278-3, SciTePress, pages 27-34. DOI: 10.5220/0006534700270034

in Bibtex Style

@conference{bioimaging18,
author={Marc Aubreville and Miguel Goncalves and Christian Knipfer and Nicolai Oetter and Tobias Würfl and Helmut Neumann and Florian Stelzle and Christopher Bohr and Andreas Maier},
title={Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-site Robustness Assessment},
booktitle={Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 2: BIOIMAGING},
year={2018},
pages={27-34},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006534700270034},
isbn={978-989-758-278-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018) - Volume 2: BIOIMAGING
TI - Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images - A Cross-site Robustness Assessment
SN - 978-989-758-278-3
AU - Aubreville M.
AU - Goncalves M.
AU - Knipfer C.
AU - Oetter N.
AU - Würfl T.
AU - Neumann H.
AU - Stelzle F.
AU - Bohr C.
AU - Maier A.
PY - 2018
SP - 27
EP - 34
DO - 10.5220/0006534700270034
PB - SciTePress