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 - 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 - TY - INPR A1 - Stoeve, Maike A1 - Aubreville, Marc A1 - Oetter, Nicolai A1 - Knipfer, Christian A1 - Neumann, Helmut A1 - Stelzle, Florian A1 - Maier, Andreas T1 - Motion Artifact Detection in Confocal Laser Endomicroscopy Images N2 - Confocal Laser Endomicroscopy (CLE), an optical imaging technique allowing non-invasive examination of the mucosa on a (sub)- cellular level, has proven to be a valuable diagnostic tool in gastroenterology and shows promising results in various anatomical regions including the oral cavity. Recently, the feasibility of automatic carcinoma detection for CLE images of sufficient quality was shown. However, in real world data sets a high amount of CLE images is corrupted by artifacts. Amongst the most prevalent artifact types are motion-induced image deteriorations. In the scope of this work, algorithmic approaches for the automatic detection of motion artifact-tainted image regions were developed. Hence, this work provides an important step towards clinical applicability of automatic carcinoma detection. Both, conventional machine learning and novel, deep learning-based approaches were assessed. The deep learning-based approach outperforms the conventional approaches, attaining an AUC of 0.90. UR - https://doi.org/10.48550/arXiv.1711.01117 Y1 - 2018 UR - https://doi.org/10.48550/arXiv.1711.01117 UR - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:bvb:573-12588 PB - arXiv CY - Ithaca ER -