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

Aubreville, Marc; Stoeve, Maike; Oetter, Nicolai; Goncalves, Miguel; Knipfer, Christian; Neumann, Helmut; Bohr, Christopher; Stelzle, Florian; Maier, Andreas

doi:10.1007/s11548-018-1836-1

Cited by 42 publications

(30 citation statements)

References 36 publications

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“…Convolutional neural networks are also very effective for other image classification tasks. In [70] they are employed to automatically detect images containing motion artifacts in confocal laser-endoscopy images.…”

Section: Image Detection and Recognitionmentioning

confidence: 99%

A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

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422

224

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This paper tries to give a gentle introduction to deep learning in medical image processing, proceeding from theoretical foundations to applications. We first discuss general reasons for the popularity of deep learning, including several major breakthroughs in computer science. Next, we start reviewing the fundamental basics of the perceptron and neural networks, along with some fundamental theory that is often omitted. Doing so allows us to understand the reasons for the rise of deep learning in many application domains. Obviously medical image processing is one of these areas which has been largely affected by this rapid progress, in particular in image detection and recognition, image segmentation, image registration, and computer-aided diagnosis. There are also recent trends in physical simulation, modelling, and reconstruction that have led to astonishing results. Yet, some of these approaches neglect prior knowledge and hence bear the risk of producing implausible results. These apparent weaknesses highlight current limitations of deep learning. However, we also briefly discuss promising approaches that might be able to resolve these problems in the future.

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Section: Image Detection and Recognitionmentioning

confidence: 99%

A gentle introduction to deep learning in medical image processing

Maier

Syben

Lasser

et al. 2019

Zeitschrift für Medizinische Physik

Self Cite

422

224

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“…Eine wichtige Voraussetzung für eine korrekte Interpretation ist die Bildqualität. Mit den derzeit verfügbaren Methoden sind die CLE-Aufnahmen häufig durch Rausch-oder Bewegungsartefakte verfälscht [36][37][38][39].…”

Section: Computergestützte Auswertungunclassified

“…Daher unterscheiden sich die verschiedenen Geräte in ihrer Anfälligkeit für Artefakte. Bildverarbeitungssysteme haben das Potenzial, alle wichtigen Artefakt-Typen zu erkennen und zu eliminieren [38,39]. Sie ermöglichen nicht nur die automatische Klassifizierung der Befunde, sondern erhöhen auch die Qualität der Bilder, um die einfache klinische Anwendung zu realisieren.…”

Section: Computergestützte Auswertungunclassified

Konfokale Laser-Endomikroskopie des Kopf-Hals-Plattenepithelkarzinoms: eine systematische Übersicht

Sievert

Auberville

Oetter

et al. 2021

Laryngorhinootologie

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Zusammenfassung Hintergrund Die konfokale Laser-Endomikroskopie (CLE) bietet mit einer bis zu 1000-fachen Vergrößerungsleistung die Möglichkeit, Interzellularräume in vivo zu erkennen. Die CLE hat sich in verschiedensten Fachbereichen bereits etabliert. Dieser Artikel gibt einen Überblick über die aktuelle Studienlage der CLE in der Diagnostik des Plattenepithelkarzinoms des Kopf-Hals-Bereichs. Material und Methoden Systematische Literaturrecherche in folgenden Online-Datenbanken: PubMed, MEDLINE, Thompson Reuters Web of Science, SPIE nach den Suchbegriffen: confocal laser endomicroscopy, CLE, endomicroscopy, head and neck, larynx. Bewertung der wissenschaftlichen Relevanz der Arbeiten nach festgelegten Kriterien. Ergebnisse Es wurden Arbeiten in Bezug auf die klinische Anwendung, die klinische Befunderhebung und die computergestützte Datenauswertung analysiert. Schlussfolgerungen In Zusammenschau der aktuell publizierten Ergebnisse verspricht die CLE ein großes Potenzial, die Diagnostik maligner Schleimhautveränderungen im Bereich des oberen Aerodigestivtrakts zu verbessern.

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“…Although this technique's potential has been recognized over the years by a few groups, it has not yet achieved a broad clinical application. A further possibility to objectivize CLE findings is utilizing automatic classification [26]. Aubreville et al showed that an approach based on transfer learning from intermediate endpoints within a pre-trained inception v3 network with preprocessing could reach an overall 94.8% accuracy, significantly improving overall performance over the traditional state of the art feature-based machine learning approaches [26].…”

Section: Discussionmentioning

confidence: 99%

Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study

Sievert

Stelzle

Aubreville

et al. 2021

Eur Arch Otorhinolaryngol

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Purpose This pilot study aimed to assess the feasibility of intraoperative assessment of safe margins with confocal laser endomicroscopy (CLE) during oropharyngeal squamous cell carcinoma (OPSCC) surgery. Methods We included five consecutive patients confirmed OPSCC and planned tumor resection in September and October 2020. Healthy appearing mucosa in the marginal zone, and the tumor margin, were examined with CLE and biopsy during tumor resection. A total of 12,809 CLE frames were correlated with the gold standard of hematoxylin and eosin staining. Three head and neck surgeons and one pathologist were asked to identify carcinoma in a sample of 169 representative images, blinded to the histological results. Results Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. CLE optical biopsy of OPSCC demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, PPV, and NPV of 86%, 90%, 79%, 88%, and 82%, respectively, with inter-rater reliability and κ-value of 0.60. Conclusion CLE can be easily integrated into the intraoperative setting, generate real-time, in-vivo microscopic images of the oropharynx for evaluation and demarcation of cancer. It can eventually contribute to a less radical approach by enabling a more precise evaluation of the cancer margin.

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

Abstract: Over traditional machine learning approaches with state-of-the-art features, we achieved significantly improved overall performance.

Cited by 42 publications

References 36 publications

A gentle introduction to deep learning in medical image processing

A gentle introduction to deep learning in medical image processing

Konfokale Laser-Endomikroskopie des Kopf-Hals-Plattenepithelkarzinoms: eine systematische Übersicht

Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study

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