Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images.

Aubreville M, Stöve M, Oetter N, Goncalves M, Knipfer C, Neumann H, Bohr C, Stelzle F, Maier A (2019)

Publication Language: English

Publication Status: Published

Publication Type: Journal article, Original article

Publication year: 2019

Journal

Book Volume: 14

Pages Range: 31-42

Journal Issue: 1

DOI: 10.1007/s11548-018-1836-1

Abstract

Probe-based confocal laser endomicroscopy (pCLE) is a subcellular in vivo imaging technique capable of producing images that enable diagnosis of malign structural modifications in epithelial tissue. Images acquired with pCLE are, however, often tainted by significant artifacts that impair diagnosis. This is especially detrimental for automated image analysis, which is why said images are often excluded from recognition pipelines.\nWe present an approach for the automatic detection of motion artifacts in pCLE images and apply this methodology to a data set of 15 thousand images of epithelial tissue acquired in the oral cavity and the vocal folds. The approach is based on transfer learning from intermediate endpoints within a pre-trained Inception v3 network with tailored preprocessing. For detection within the non-rectangular pCLE images, we perform pooling within the activation maps of the network and evaluate this at different network depths.\nWe achieved area under the ROC curve values of 0.92 with the proposed method, compared to 0.80 for the best feature-based machine learning approach. Our overall accuracy with the presented approach is 94.8%.\nOver traditional machine learning approaches with state-of-the-art features, we achieved significantly improved overall performance.\nPURPOSE\nMETHODS\nRESULTS\nCONCLUSION

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Involved external institutions

Johannes Gutenberg-Universität Mainz (JGU) Germany (DE) Universitätsklinikum Regensburg Germany (DE) Universitätsklinikum Hamburg-Eppendorf (UKE) Germany (DE)

How to cite

APA:

Aubreville, M., Stöve, M., Oetter, N., Goncalves, M., Knipfer, C., Neumann, H.,... Maier, A. (2019). Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images. International Journal of Computer Assisted Radiology and Surgery, 14(1), 31-42. https://doi.org/10.1007/s11548-018-1836-1

MLA:

Aubreville, Marc, et al. "Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images." International Journal of Computer Assisted Radiology and Surgery 14.1 (2019): 31-42.

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