Motion Artifact Detection in Confocal Laser Endomicroscopy Images

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

doi:10.1007/978-3-662-56537-7_85

Cited by 7 publications

(5 citation statements)

References 9 publications

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“…Although these motion artefacts are relatively easy for a human examiner to overlook, they represent a relevant interfering factor in automatic analysis. Detection of motion artefacts demonstrated that the performance can be improved by pattern recognition algorithms that recognise malignant changes 34 . For comparison, the results presented in this study gained by two trained and certified examiners show an accuracy of 91.38-96.55%.…”

Section: Discussionmentioning

confidence: 99%

Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds

Goncalves

Aubreville

Müller

et al. 2019

Acta Otorhinolaryngol Ital

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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. KEY WORDS: Confocal laser endomicroscopy • Laryngeal cancer • Non-invasive histological imaging • Vocal folds RIASSUNTO L'endomicroscopia confocale laser (CLE) tramite sonda è una tecnica innovativa per l'analisi in tempo reale non invasiva dell'epitelio di superficie. Mentre il suo uso diagnostico ha avuto successo tra gli specialisti, questo metodo non si è ancora affermato nella routine clinica, in parte anche per la mancanza di regole e criteri per la classificazione delle varie lesioni. Il nostro obiettivo era quello di determinare il valore diagnostico e l'affidabilità tra valutatori CLE nel rilevamento delle lesioni maligne delle corde vocali. Sono state estratte 58 sequenze video dagli esami CLE tramite sonda (sonda GastroFlex con un sistema laser Cellvizio ®) di 3 pazienti con carcinoma squamocellulare e di 4 pazienti con alterazioni benigne delle corde vocali. A due chirurghi ORL, in cieco per l'esame istologico, è stato chiesto di identificare le sequenze riguardanti un carcinoma.

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Section: Discussionmentioning

confidence: 99%

Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds

Goncalves

Aubreville

Müller

et al. 2019

Acta Otorhinolaryngol Ital

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“…Electronics 2024, 13, 724 2 of 13 Researchers have carried out a lot of valuable work to automatically assess CT image quality [4][5][6][7][8][9]. Several studies have mainly focused on handcrafted feature designation.…”

Section: Introductionmentioning

confidence: 99%

Motion Artifact Detection Based on Regional–Temporal Graph Attention Network from Head Computed Tomography Images

Liu,

Wen,

2024

Electronics

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Artifacts are the main cause of degradation in CT image quality and diagnostic accuracy. Because of the complex texture of CT images, it is a challenging task to automatically detect artifacts from limited image samples. Recently, graph convolutional networks (GCNs) have achieved great success and shown promising results in medical imaging due to their powerful learning ability. However, GCNs do not take the attention mechanism into consideration. To overcome their limitations, we propose a novel Regional–Temporal Graph Attention Network for motion artifact detection from computed tomography images (RT-GAT). In this paper, head CT images are viewed as a heterogeneous graph by taking regional and temporal information into consideration, and the graph attention network is utilized to extract the features of the constructed graph. Then, the feature vector is input into the classifier to detect the motion artifacts. The experimental results demonstrate that our proposed RT-GAT method outperforms the state-of-the-art methods on a real-world CT dataset.

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“…For medical image quality assess and artifacts detection [ 26 , 27 , 28 ], there are still three major challenges remaining.…”

Section: Introductionmentioning

confidence: 99%

Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images

Liu

Wen

Sun

et al. 2022

Sensors

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Computed tomography (CT) images play an important role due to effectiveness and accessibility, however, motion artifacts may obscure or simulate pathology and dramatically degrade the diagnosis accuracy. In recent years, convolutional neural networks (CNNs) have achieved state-of-the-art performance in medical imaging due to the powerful learning ability with the help of the advanced hardware technology. Unfortunately, CNNs have significant overhead on memory usage and computational resources and are labeled ‘black-box’ by scholars for their complex underlying structures. To this end, an interpretable graph-based method has been proposed for motion artifacts detection from head CT images in this paper. From a topological perspective, the artifacts detection problem has been reformulated as a complex network classification problem based on the network topological characteristics of the corresponding complex networks. A motion artifacts detection method based on complex networks (MADM-CN) has been proposed. Firstly, the graph of each CT image is constructed based on the theory of complex networks. Secondly, slice-to-slice relationship has been explored by multiple graph construction. In addition, network topological characteristics are investigated locally and globally, consistent topological characteristics including average degree, average clustering coefficient have been utilized for classification. The experimental results have demonstrated that the proposed MADM-CN has achieved better performance over conventional machine learning and deep learning methods on a real CT dataset, reaching up to 98% of the accuracy and 97% of the sensitivity.

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Motion Artifact Detection in Confocal Laser Endomicroscopy Images

Cited by 7 publications

References 9 publications

Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds

Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds

Motion Artifact Detection Based on Regional–Temporal Graph Attention Network from Head Computed Tomography Images

Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images

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