@article{GoncalvesAubrevilleMuelleretal.2019, author = {Goncalves, Miguel and Aubreville, Marc and M{\"u}ller, Sarina K. and Sievert, Matti and Maier, Andreas and Iro, Heinrich and Bohr, Christopher}, title = {Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds}, volume = {39}, journal = {ACTA Otorhinolaryngologica Italica}, number = {6}, publisher = {Pacini}, address = {Pisa}, issn = {1827-675X}, doi = {https://doi.org/10.14639/0392-100X-2121}, pages = {389 -- 395}, year = {2019}, abstract = {Probe-based confocal laser endomicroscopy (CLE) is an innovative technique for real-time, non-invasive analysis of the surface epithelium. While being successfully used for diagnosis by experts, this method has not yet been established in clinical routine, partly due to the lack of standards and criteria for classifying various lesions. Our aim was to determine the diagnostic value and inter-rater reliability of CLE in detecting malignant lesions of the vocal cords. 58 video sequences were extracted from the probe-based CLE (GastroFlex probe with a Cellvizio® laser system) examinations of 3 patients with squamous cell carcinomas and 4 patients with benign alterations of the vocal folds. Two ENT surgeons, who were blinded to the histological result, were asked to identify the sequences representing a carcinoma. We showed an accuracy, sensitivity, specificity, PPV and NPV of 91.38-96.55\%, 100\%, 87.8-95.2\%, 77.27-89.47\% and 100\%, respectively, with an inter-rater reliability of k = 0.89 ("almost perfect agreement"). Probe-based CLE is a promising method for diagnosis and assessment of vocal fold lesions in vivo. Our results suggest that, with adequate training, the diagnostic value of this technique can be improved and potentially provide important information during oncological surgery.}, language = {en} } @article{AubrevilleKnipferOetteretal.2017, author = {Aubreville, Marc and Knipfer, Christian and Oetter, Nicolai and Jaremenko, Christian and Rodner, Erik and Denzler, Joachim and Bohr, Christopher and Neumann, Helmut and Stelzle, Florian and Maier, Andreas}, title = {Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning}, volume = {7}, pages = {11979}, journal = {Scientific Reports}, publisher = {Springer Nature}, address = {London}, issn = {2045-2322}, doi = {https://doi.org/10.1038/s41598-017-12320-8}, year = {2017}, abstract = {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\%).}, language = {en} } @article{AubrevilleStoeveOetteretal.2019, author = {Aubreville, Marc and Stoeve, Maike and Oetter, Nicolai and Goncalves, Miguel and Knipfer, Christian and Neumann, Helmut and Bohr, Christopher and Stelzle, Florian and Maier, Andreas}, title = {Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images}, volume = {14}, journal = {International Journal of Computer Assisted Radiology and Surgery}, number = {1}, publisher = {Springer}, address = {Berlin}, issn = {1861-6429}, doi = {https://doi.org/10.1007/s11548-018-1836-1}, pages = {31 -- 42}, year = {2019}, language = {en} } @inproceedings{AubrevilleGoncalvesKnipferetal.2018, author = {Aubreville, Marc and Goncalves, Miguel and Knipfer, Christian and Oetter, Nicolai and W{\"u}rfl, Tobias and Neumann, Helmut and Stelzle, Florian and Bohr, Christopher and Maier, Andreas}, title = {Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images}, booktitle = {Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies}, subtitle = {A Cross-Site Robustness Assessment}, editor = {Wiebe, Sheldon and Gamboa, Hugo and Fred, Ana and Berm{\´u}dez i Badia, Sergi}, publisher = {SciTePress}, address = {Set{\´u}bal}, isbn = {978-989-758-278-3}, doi = {https://doi.org/10.5220/0006534700270034}, pages = {27 -- 34}, year = {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 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.}, language = {en} } @inproceedings{AubrevilleGoncalvesKnipferetal.2019, author = {Aubreville, Marc and Goncalves, Miguel and Knipfer, Christian and Oetter, Nicolai and W{\"u}rfl, Tobias and Neumann, Helmut and Stelzle, Florian and Bohr, Christopher and Maier, Andreas}, title = {Transferability of deep learning algorithms for malignancy detection in confocal laser endomicroscopy images from different anatomical locations of the upper gastrointestinal tract}, booktitle = {Biomedical Engineering Systems and Technologies}, publisher = {Springer}, address = {Cham}, isbn = {978-3-030-29195-2}, issn = {1865-0929}, doi = {https://doi.org/10.1007/978-3-030-29196-9_4}, pages = {67 -- 85}, year = {2019}, language = {en} }