TY - JOUR A1 - Aubreville, Marc A1 - Knipfer, Christian A1 - Oetter, Nicolai A1 - Jaremenko, Christian A1 - Rodner, Erik A1 - Denzler, Joachim A1 - Bohr, Christopher A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Maier, Andreas T1 - Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning JF - Scientific Reports N2 - Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%). UR - https://doi.org/10.1038/s41598-017-12320-8 Y1 - 2017 UR - https://doi.org/10.1038/s41598-017-12320-8 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12464 SN - 2045-2322 VL - 7 PB - Springer Nature CY - London ER - TY - JOUR A1 - Aubreville, Marc A1 - Stoeve, Maike A1 - Oetter, Nicolai A1 - Goncalves, Miguel A1 - Knipfer, Christian A1 - Neumann, Helmut A1 - Bohr, Christopher A1 - Stelzle, Florian A1 - Maier, Andreas T1 - Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images JF - International Journal of Computer Assisted Radiology and Surgery UR - https://doi.org/10.1007/s11548-018-1836-1 KW - Deep convolutional neural networks KW - Confocal laser endomicroscopy KW - Motion artifact detection Y1 - 2019 UR - https://doi.org/10.1007/s11548-018-1836-1 SN - 1861-6429 VL - 14 IS - 1 SP - 31 EP - 42 PB - Springer CY - Berlin ER - TY - CHAP A1 - Aubreville, Marc A1 - Goncalves, Miguel A1 - Knipfer, Christian A1 - Oetter, Nicolai A1 - Würfl, Tobias A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Bohr, Christopher A1 - Maier, Andreas ED - Wiebe, Sheldon ED - Gamboa, Hugo ED - Fred, Ana ED - Bermúdez i Badia, Sergi T1 - Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images BT - A Cross-Site Robustness Assessment T2 - Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies N2 - 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 accurac y 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. UR - https://doi.org/10.5220/0006534700270034 KW - Automatic Carcinoma Detection KW - Confocal Laser Endomicroscopy KW - Deep Convolutional Networks KW - Squamous Cell Carcinoma Y1 - 2018 UR - https://doi.org/10.5220/0006534700270034 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-26277 SN - 978-989-758-278-3 SP - 27 EP - 34 PB - SciTePress CY - Setúbal ER - TY - CHAP A1 - Aubreville, Marc A1 - Goncalves, Miguel A1 - Knipfer, Christian A1 - Oetter, Nicolai A1 - Würfl, Tobias A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Bohr, Christopher A1 - Maier, Andreas T1 - Transferability of deep learning algorithms for malignancy detection in confocal laser endomicroscopy images from different anatomical locations of the upper gastrointestinal tract T2 - Biomedical Engineering Systems and Technologies UR - https://doi.org/10.1007/978-3-030-29196-9_4 KW - Confocal Laser Endomicroscopy KW - Transfer learning KW - Head and neck squamous cell carcinoma Y1 - 2019 UR - https://doi.org/10.1007/978-3-030-29196-9_4 SN - 978-3-030-29195-2 SN - 978-3-030-29196-9 SN - 1865-0929 N1 - Access to this content is enabled by Nationallizenz Ebooks Medicine SP - 67 EP - 85 PB - Springer CY - Cham ER - TY - CHAP A1 - Stoeve, Maike A1 - Aubreville, Marc A1 - Oetter, Nicolai A1 - Knipfer, Christian A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Maier, Andreas ED - Maier, Andreas ED - Deserno, Thomas Martin ED - Handels, Heinz ED - Maier-Hein, Klaus H. ED - Palm, Christoph ED - Tolxdorff, Thomas T1 - Motion Artifact Detection in Confocal Laser Endomicroscopy Images T2 - Bildverarbeitung für die Medizin 2018: Algorithmen - Systeme - Anwendungen UR - https://doi.org/10.1007/978-3-662-56537-7_85 Y1 - 2018 UR - https://doi.org/10.1007/978-3-662-56537-7_85 SN - 978-3-662-56537-7 SP - 328 EP - 333 PB - Springer Vieweg CY - Berlin ER -