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Motion artifact detection in confocal laser endomicroscopy images

Standard

Motion artifact detection in confocal laser endomicroscopy images. / Stoeve, Maike; Aubreville, Marc; Oetter, Nicolai; Knipfer, Christian; Neumann, Helmut; Stelzle, Florian; Maier, Andreas.

in: Informatik aktuell, Nr. 211279, 2018, S. 328-333.

Publikationen: SCORING: Beitrag in Fachzeitschrift/Zeitung › Konferenzaufsatz in Fachzeitschrift › Forschung › Begutachtung

Harvard

Stoeve, M, Aubreville, M, Oetter, N, Knipfer, C, Neumann, H, Stelzle, F & Maier, A 2018, 'Motion artifact detection in confocal laser endomicroscopy images', Informatik aktuell, Nr. 211279, S. 328-333. https://doi.org/10.1007/978-3-662-56537-7_85

APA

Stoeve, M., Aubreville, M., Oetter, N., Knipfer, C., Neumann, H., Stelzle, F., & Maier, A. (2018). Motion artifact detection in confocal laser endomicroscopy images. Informatik aktuell, (211279), 328-333. https://doi.org/10.1007/978-3-662-56537-7_85

Vancouver

Stoeve M, Aubreville M, Oetter N, Knipfer C, Neumann H, Stelzle F et al. Motion artifact detection in confocal laser endomicroscopy images. Informatik aktuell. 2018;(211279):328-333. https://doi.org/10.1007/978-3-662-56537-7_85

Bibtex

@article{b9b3863333094f3e929ce3f3dc6258cb,
title = "Motion artifact detection in confocal laser endomicroscopy images",
abstract = "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.",
author = "Maike Stoeve and Marc Aubreville and Nicolai Oetter and Christian Knipfer and Helmut Neumann and Florian Stelzle and Andreas Maier",
year = "2018",
doi = "10.1007/978-3-662-56537-7_85",
language = "English",
pages = "328--333",
number = "211279",
note = "Workshop on Bildverarbeitung fur die Medizin, 2018 ; Conference date: 11-03-2018 Through 13-03-2018",

}

RIS

TY - JOUR

T1 - Motion artifact detection in confocal laser endomicroscopy images

AU - Stoeve, Maike

AU - Aubreville, Marc

AU - Oetter, Nicolai

AU - Knipfer, Christian

AU - Neumann, Helmut

AU - Stelzle, Florian

AU - Maier, Andreas

PY - 2018

Y1 - 2018

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.

AB - 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 - http://www.scopus.com/inward/record.url?scp=85043326711&partnerID=8YFLogxK

U2 - 10.1007/978-3-662-56537-7_85

DO - 10.1007/978-3-662-56537-7_85

M3 - Conference article in journal

AN - SCOPUS:85043326711

SP - 328

EP - 333

IS - 211279

T2 - Workshop on Bildverarbeitung fur die Medizin, 2018

Y2 - 11 March 2018 through 13 March 2018

ER -