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Oetter N, Pröll J, Sievert M, Goncalves M, Rohde M, Nobis CP, Knipfer C, Aubreville M, Pan Z, Breininger K, Maier A, Kesting M, Stelzle F. Oral mucosa - an examination map for confocal laser endomicroscopy within the oral cavity: an experimental clinical study. Clin Oral Investig 2024; 28:266. [PMID: 38652317 PMCID: PMC11039507 DOI: 10.1007/s00784-024-05664-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2024] [Accepted: 04/16/2024] [Indexed: 04/25/2024]
Abstract
OBJECTIVES Confocal laser endomicroscopy (CLE) is an optical method that enables microscopic visualization of oral mucosa. Previous studies have shown that it is possible to differentiate between physiological and malignant oral mucosa. However, differences in mucosal architecture were not taken into account. The objective was to map the different oral mucosal morphologies and to establish a "CLE map" of physiological mucosa as baseline for further application of this powerful technology. MATERIALS AND METHODS The CLE database consisted of 27 patients. The following spots were examined: (1) upper lip (intraoral) (2) alveolar ridge (3) lateral tongue (4) floor of the mouth (5) hard palate (6) intercalary line. All sequences were examined by two CLE experts for morphological differences and video quality. RESULTS Analysis revealed clear differences in image quality and possibility of depicting tissue morphologies between the various localizations of oral mucosa: imaging of the alveolar ridge and hard palate showed visually most discriminative tissue morphology. Labial mucosa was also visualized well using CLE. Here, typical morphological features such as uniform cells with regular intercellular gaps and vessels could be clearly depicted. Image generation and evaluation was particularly difficult in the area of the buccal mucosa, the lateral tongue and the floor of the mouth. CONCLUSION A physiological "CLE map" for the entire oral cavity could be created for the first time. CLINICAL RELEVANCE This will make it possible to take into account the existing physiological morphological features when differentiating between normal mucosa and oral squamous cell carcinoma in future work.
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Affiliation(s)
- Nicolai Oetter
- Department of Oral and Maxillofacial Surgery, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), University Hospital Erlangen, Glückstraße 11, 91054, Erlangen, Germany.
- SAOT‑Erlangen Graduate School in Advanced Optical Technologies, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), Paul Gordan Straße 6, 91052, Erlangen, Germany.
| | - Jonas Pröll
- Department of Oral and Maxillofacial Surgery, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), University Hospital Erlangen, Glückstraße 11, 91054, Erlangen, Germany
| | - Matti Sievert
- Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), University Hospital Erlangen, Waldstraße 1, 91054, Erlangen, Germany
| | - Miguel Goncalves
- Department of Otorhinolaryngology, Head and Neck Surgery, Julius-Maximilians University Würzburg, University Hospital Würzburg, Josef-Schneider-Straße 11, 97080, Würzburg, Germany
| | - Maximilian Rohde
- Department of Oral and Maxillofacial Surgery, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), University Hospital Erlangen, Glückstraße 11, 91054, Erlangen, Germany
- SAOT‑Erlangen Graduate School in Advanced Optical Technologies, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), Paul Gordan Straße 6, 91052, Erlangen, Germany
| | - Christopher-Philipp Nobis
- Department of Oral and Maxillofacial Surgery, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), University Hospital Erlangen, Glückstraße 11, 91054, Erlangen, Germany
| | - Christian Knipfer
- Department of Oral and Maxillofacial Surgery, University Hamburg, University Medical Center Hamburg- Eppendorf, Martinistraße 52, 20246, Hamburg, Germany
| | - Marc Aubreville
- Technische Hochschule Ingolstadt, Esplanade 10, 85049, Ingolstadt, Germany
| | - Zhaoya Pan
- Pattern Recognition Lab, Department of Computer Science, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), Martensstraße 3, 91058, Erlangen, Germany
| | - Katharina Breininger
- Department Artificial Intelligence in Biomedical Engineering, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), Henkestraße 91, 91052, Erlangen, Germany
| | - Andreas Maier
- Pattern Recognition Lab, Department of Computer Science, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), Martensstraße 3, 91058, Erlangen, Germany
| | - Marco Kesting
- Department of Oral and Maxillofacial Surgery, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), University Hospital Erlangen, Glückstraße 11, 91054, Erlangen, Germany
- SAOT‑Erlangen Graduate School in Advanced Optical Technologies, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), Paul Gordan Straße 6, 91052, Erlangen, Germany
| | - Florian Stelzle
- Department of Oral and Maxillofacial Surgery, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), University Hospital Erlangen, Glückstraße 11, 91054, Erlangen, Germany
- SAOT‑Erlangen Graduate School in Advanced Optical Technologies, Friedrich‑Alexander University Erlangen‑Nürnberg (FAU), Paul Gordan Straße 6, 91052, Erlangen, Germany
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Liu Y, Wen T, Sun W, Liu Z, Song X, He X, Zhang S, Wu Z. Graph-Based Motion Artifacts Detection Method from Head Computed Tomography Images. SENSORS (BASEL, SWITZERLAND) 2022; 22:5666. [PMID: 35957222 PMCID: PMC9371218 DOI: 10.3390/s22155666] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 06/22/2022] [Revised: 07/26/2022] [Accepted: 07/27/2022] [Indexed: 06/15/2023]
Abstract
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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Affiliation(s)
- Yiwen Liu
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China;
| | - Tao Wen
- School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China;
- Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China; (Z.L.); (X.S.)
| | - Wei Sun
- School of Computer Science, Neusoft Institute Guangdong, Foshan 528225, China;
| | - Zhenyu Liu
- Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China; (Z.L.); (X.S.)
| | - Xiaoying Song
- Department of Computer Science and Technology, Dalian Neusoft University of Information, Dalian 116023, China; (Z.L.); (X.S.)
| | - Xuan He
- College of Medicine and Biological Information Engineering, Northeastern University, Shenyang 110819, China;
| | - Shuo Zhang
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (S.Z.); (Z.W.)
| | - Zhenning Wu
- School of Information Science and Engineering, Northeastern University, Shenyang 110819, China; (S.Z.); (Z.W.)
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Goncalves M, Aubreville M, Mueller SK, Sievert M, Maier A, Iro H, Bohr C. Probe-based confocal laser endomicroscopy in detecting malignant lesions of vocal folds. ACTA ACUST UNITED AC 2019; 39:389-395. [PMID: 30745593 PMCID: PMC6966779 DOI: 10.14639/0392-100x-2121] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/17/2018] [Accepted: 04/10/2018] [Indexed: 11/23/2022]
Abstract
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.
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Affiliation(s)
- M Goncalves
- Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg, University Hospital, Erlangen, Germany
| | - M Aubreville
- Pattern Recognition Lab, Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - S K Mueller
- Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg, University Hospital, Erlangen, Germany
| | - M Sievert
- Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg, University Hospital, Erlangen, Germany
| | - A Maier
- Pattern Recognition Lab, Computer Science, Friedrich-Alexander-Universität Erlangen-Nürnberg, Erlangen, Germany
| | - H Iro
- Department of Otorhinolaryngology, Head and Neck Surgery, Friedrich-Alexander-Universität Erlangen-Nürnberg, University Hospital, Erlangen, Germany
| | - C Bohr
- Department of Otorhinolaryngology, Head and Neck Surgery, Universität Regensburg, University Hospital, Regensburg, Germany
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Aubreville M, Stoeve M, Oetter N, Goncalves M, Knipfer C, Neumann H, Bohr C, Stelzle F, Maier A. Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images. Int J Comput Assist Radiol Surg 2018; 14:31-42. [DOI: 10.1007/s11548-018-1836-1] [Citation(s) in RCA: 23] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/04/2018] [Accepted: 07/26/2018] [Indexed: 12/11/2022]
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