<?xml version="1.0" encoding="utf-8"?>
<export-example>
  <doc>
    <id>1246</id>
    <completedYear>2017</completedYear>
    <publishedYear>2017</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>10</pageNumber>
    <edition/>
    <issue/>
    <volume>7</volume>
    <articleNumber>11979</articleNumber>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>London</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2021-02-23</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Automatic classification of cancerous tissue in laserendomicroscopy images of the oral cavity using deep learning</title>
    <abstract language="eng">Oral Squamous Cell Carcinoma (OSCC) is a common type of cancer of the oral epithelium. Despite their high impact on mortality, sufficient screening methods for early diagnosis of OSCC often lack accuracy and thus OSCCs are mostly diagnosed at a late stage. Early detection and accurate outline estimation of OSCCs would lead to a better curative outcome and a reduction in recurrence rates after surgical treatment. Confocal Laser Endomicroscopy (CLE) records sub-surface micro-anatomical images for in vivo cell structure analysis. Recent CLE studies showed great prospects for a reliable, real-time ultrastructural imaging of OSCC in situ. We present and evaluate a novel automatic approach for OSCC diagnosis using deep learning technologies on CLE images. The method is compared against textural feature-based machine learning approaches that represent the current state of the art. For this work, CLE image sequences (7894 images) from patients diagnosed with OSCC were obtained from 4 specific locations in the oral cavity, including the OSCC lesion. The present approach is found to outperform the state of the art in CLE image recognition with an area under the curve (AUC) of 0.96 and a mean accuracy of 88.3% (sensitivity 86.6%, specificity 90%).</abstract>
    <parentTitle language="eng">Scientific Reports</parentTitle>
    <identifier type="urn">urn:nbn:de:bvb:573-12464</identifier>
    <identifier type="issn">2045-2322</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1038/s41598-017-12320-8</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Knipfer</last_name>
    </author>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Jaremenko</last_name>
    </author>
    <author>
      <first_name>Erik</first_name>
      <last_name>Rodner</last_name>
    </author>
    <author>
      <first_name>Joachim</first_name>
      <last_name>Denzler</last_name>
    </author>
    <author>
      <first_name>Christopher</first_name>
      <last_name>Bohr</last_name>
    </author>
    <author>
      <first_name>Helmut</first_name>
      <last_name>Neumann</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1246/s41598-017-12320-8-2.pdf</file>
  </doc>
  <doc>
    <id>2627</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>27</pageFirst>
    <pageLast>34</pageLast>
    <pageNumber/>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>conferenceobject</type>
    <publisherName>SciTePress</publisherName>
    <publisherPlace>Setúbal</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-08-09</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Patch-based Carcinoma Detection on Confocal Laser Endomicroscopy Images</title>
    <abstract language="deu">Deep learning technologies such as convolutional neural networks (CNN) provide powerful methods for image recognition and have recently been employed in the field of automated carcinoma detection in confocal laser endomicroscopy (CLE) images. CLE is a (sub-)surface microscopic imaging technique that reaches magnifications of up to 1000x and is thus suitable for in vivo structural tissue analysis. In this work, we aim to evaluate the prospects of a priorly developed deep learning-based algorithm targeted at the identification of oral squamous cell carcinoma with regard to its generalization to further anatomic locations of squamous cell carcinomas in the area of head and neck. We applied the algorithm on images acquired from the vocal fold area of five patients with histologically verified squamous cell carcinoma and presumably healthy control images of the clinically normal contra-lateral vocal cord. We find that the network trained on the oral cavity data reaches an accurac y of 89.45% and an area-under-the-curve (AUC) value of 0.955, when applied on the vocal cords data. Compared to the state of the art, we achieve very similar results, yet with an algorithm that was trained on a completely disjunct data set. Concatenating both data sets yielded further improvements in cross-validation with an accuracy of 90.81% and AUC of 0.970. In this study, for the first time to our knowledge, a deep learning mechanism for the identification of oral carcinomas using CLE Images could be applied to other disciplines in the area of head and neck. This study shows the prospect of the algorithmic approach to generalize well on other malignant entities of the head and neck, regardless of the anatomical location and furthermore in an examiner-independent manner.</abstract>
    <parentTitle language="eng">Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies</parentTitle>
    <subTitle language="eng">A Cross-Site Robustness Assessment</subTitle>
    <identifier type="isbn">978-989-758-278-3</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-26277</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.5220/0006534700270034</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_conferenceName">11th International Joint Conference on Biomedical Engineering Systems and Technologies - BIOIMAGING, Funchal (Portugal), 19.-21.01.2018</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <editor>
      <first_name>Sheldon</first_name>
      <last_name>Wiebe</last_name>
    </editor>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Goncalves</last_name>
    </author>
    <editor>
      <first_name>Hugo</first_name>
      <last_name>Gamboa</last_name>
    </editor>
    <author>
      <first_name>Christian</first_name>
      <last_name>Knipfer</last_name>
    </author>
    <editor>
      <first_name>Ana</first_name>
      <last_name>Fred</last_name>
    </editor>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <editor>
      <first_name>Sergi</first_name>
      <last_name>Bermúdez i Badia</last_name>
    </editor>
    <author>
      <first_name>Tobias</first_name>
      <last_name>Würfl</last_name>
    </author>
    <author>
      <first_name>Helmut</first_name>
      <last_name>Neumann</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <author>
      <first_name>Christopher</first_name>
      <last_name>Bohr</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Automatic Carcinoma Detection</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Confocal Laser Endomicroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Deep Convolutional Networks</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Squamous Cell Carcinoma</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2627/65347.pdf</file>
  </doc>
  <doc>
    <id>1258</id>
    <completedYear/>
    <publishedYear>2018</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>2022-02-15</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Motion Artifact Detection in Confocal Laser Endomicroscopy Images</title>
    <abstract language="eng">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.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-12588</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.1711.01117</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_furtherversion">https://opus4.kobv.de/opus4-haw/frontdoor/index/index/docId/2203</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Maike</first_name>
      <last_name>Stoeve</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Knipfer</last_name>
    </author>
    <author>
      <first_name>Helmut</first_name>
      <last_name>Neumann</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1258/1711.01117.pdf</file>
  </doc>
  <doc>
    <id>4152</id>
    <completedYear/>
    <publishedYear>2023</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>6</pageNumber>
    <edition/>
    <issue/>
    <volume/>
    <articleNumber/>
    <type>preprint</type>
    <publisherName>arXiv</publisherName>
    <publisherPlace>Ithaca</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>0</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Few Shot Learning for the Classification of Confocal Laser Endomicroscopy Images of Head and Neck Tumors</title>
    <abstract language="eng">The surgical removal of head and neck tumors requires safe margins, which are usually confirmed intraoperatively by means of frozen sections. This method is, in itself, an oversampling procedure, which has a relatively low sensitivity compared to the definitive tissue analysis on paraffin-embedded sections. Confocal laser endomicroscopy (CLE) is an in-vivo imaging technique that has shown its potential in the live optical biopsy of tissue. An automated analysis of this notoriously difficult to interpret modality would help surgeons. However, the images of CLE show a wide variability of patterns, caused both by individual factors but also, and most strongly, by the anatomical structures of the imaged tissue, making it a challenging pattern recognition task. In this work, we evaluate four popular few shot learning (FSL) methods towards their capability of generalizing to unseen anatomical domains in CLE images. We evaluate this on images of sinunasal tumors (SNT) from five patients and on images of the vocal folds (VF) from 11 patients using a cross-validation scheme. The best respective approach reached a median accuracy of 79.6% on the rather homogeneous VF dataset, but only of 61.6% for the highly diverse SNT dataset. Our results indicate that FSL on CLE images is viable, but strongly affected by the number of patients, as well as the diversity of anatomical patterns.</abstract>
    <identifier type="urn">urn:nbn:de:bvb:573-41522</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.48550/arXiv.2311.07216</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">nein</enrichment>
    <licence>Creative Commons BY-SA 4.0</licence>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Zhaoya</first_name>
      <last_name>Pan</last_name>
    </author>
    <author>
      <first_name>Matti</first_name>
      <last_name>Sievert</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Ammeling</last_name>
    </author>
    <author>
      <first_name>Jonathan</first_name>
      <last_name>Ganz</last_name>
    </author>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <author>
      <first_name>Ann-Kathrin</first_name>
      <last_name>Frenken</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Goncalves</last_name>
    </author>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/4152/2311.07216.pdf</file>
  </doc>
  <doc>
    <id>2917</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>26</pageFirst>
    <pageLast>33</pageLast>
    <pageNumber/>
    <edition/>
    <issue>1</issue>
    <volume>42</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Pacini</publisherName>
    <publisherPlace>Pisa</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-10-21</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Systematic interpretation of confocal laser endomicroscopy: larynx and pharynx confocal imaging score</title>
    <abstract language="eng">Objective. Development and validation of a confocal laser endomicroscopy (CLE) classification score for the larynx and pharynx.&#13;
&#13;
Methods. Thirteen patients (154 video sequences, 9240 images) with laryngeal or pharyngeal SCC were included in this prospective study between October 2020 and February 2021. Each CLE sequence was correlated with the gold standard of histopathological examination. Based on a dataset of 94 video sequences (5640 images), a scoring system was developed. In the remaining 60 sequences (3600 images), the score was validated by four CLE experts and four head and neck surgeons who were not familiar with CLE.&#13;
&#13;
Results. Tissue homogeneity, cell size, borders and clusters, capillary loops and the nucleus/ cytoplasm ratio were defined as the scoring criteria. Using this score, the CLE experts obtained an accuracy, sensitivity, and specificity of 90.8%, 95.1%, and 86.4%, respectively, and the CLE non-experts of 86.2%, 86.4%, and 86.1%. Interobserver agreement Fleiss’ kappa was 0.8 and 0.6, respectively.&#13;
&#13;
Conclusions. CLE can be reliably evaluated based on defined and reproducible imaging features, which demonstrate a high diagnostic value. CLE can be easily integrated into the intraoperative setting and generate real-time, in-vivo microscopic images to demarcate malignant changes.</abstract>
    <parentTitle language="eng">Acta otorhinolaryngologica italica</parentTitle>
    <identifier type="issn">1827-675X</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-29175</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.14639/0392-100X-N1643</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY-NC-ND 4.0</licence>
    <author>
      <first_name>Matti</first_name>
      <last_name>Sievert</last_name>
    </author>
    <author>
      <first_name>Konstantinos</first_name>
      <last_name>Mantsopoulos</last_name>
    </author>
    <author>
      <first_name>Sarina K.</first_name>
      <last_name>Müller</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Eckstein</last_name>
    </author>
    <author>
      <first_name>Robin</first_name>
      <last_name>Rupp</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Heinrich</first_name>
      <last_name>Iro</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Goncalves</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>confocal laser endomicroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>head and neck cancer</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>classification system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>non-invasive histological imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>larynx</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>pharynx</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2917/1643-ArticleText-17164-2-10-20220307.pdf</file>
  </doc>
  <doc>
    <id>2318</id>
    <completedYear/>
    <publishedYear>2022</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4147</pageFirst>
    <pageLast>4156</pageLast>
    <pageNumber/>
    <edition/>
    <issue>8</issue>
    <volume>279</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Validity of tissue homogeneity in confocal laser endomicroscopy on the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma</title>
    <abstract language="eng">Purpose&#13;
Confocal laser endomicroscopy (CLE) allows imaging of the laryngeal mucosa in a thousand-fold magnification. This study analyzes differences in tissue homogeneity between healthy mucosa and squamous cell carcinoma (SCC) via CLE.&#13;
&#13;
Materials and methods&#13;
We included five SCC patients with planned total laryngectomy in this study between October 2020 and February 2021. We captured CLE scans of the tumor and healthy mucosa. Analysis of image homogeneity to diagnose SCC was performed by measuring the signal intensity in four regions of interest (ROI) in each frame in a total of 60 sequences. Each sequence was assigned to the corresponding histological pattern, derived from hematoxylin and eosin staining. In addition, we recorded the subjective evaluation of seven investigators regarding tissue homogeneity.&#13;
&#13;
Results&#13;
Out of 3600 images, 1620 (45%) correlated with benign mucosa and 1980 (55%) with SCC. ROIs of benign mucosa and SCC had a mean and standard deviation (SD) of signal intensity of, respectively, 232.1 ± 3.34 and 467.3 ± 9.72 (P &lt; 0.001). The mean SD between the four different ROIs was 39.1 ± 1.03 for benign and 101.5 ± 2.6 for SCC frames (P &lt; 0.001). In addition, homogeneity yielded a sensitivity and specificity of 81.8% and 86.2%, respectively, regarding the investigator-dependent analysis.&#13;
&#13;
Conclusions&#13;
SCC shows a significant tissue inhomogeneity in comparison to the healthy epithelium. The results support this feature’s importance in identifying malignant mucosa areas during CLE examination. However, the examiner-dependent evaluation emphasizes that homogeneity is a sub-criterion that must be considered in a broad context.</abstract>
    <parentTitle language="eng">European Archives of Oto-Rhino-Laryngology and Head &amp; Neck</parentTitle>
    <identifier type="issn">1434-4726</identifier>
    <identifier type="issn">0937-4477</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-23180</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/s00405-022-07304-y</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Matti</first_name>
      <last_name>Sievert</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Antoniu-Oreste</first_name>
      <last_name>Gostian</last_name>
    </author>
    <author>
      <first_name>Konstantinos</first_name>
      <last_name>Mantsopoulos</last_name>
    </author>
    <author>
      <first_name>Michael</first_name>
      <last_name>Koch</last_name>
    </author>
    <author>
      <first_name>Sarina K.</first_name>
      <last_name>Müller</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Eckstein</last_name>
    </author>
    <author>
      <first_name>Robin</first_name>
      <last_name>Rupp</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Heinrich</first_name>
      <last_name>Iro</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Goncalves</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>confocal laser endomicroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>head and neck cancer</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>classification system</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>non-invasive histological imaging</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>larynx</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>pharynx</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2318/Sievert2022_ValidityOfTissueHomogeneity.pdf</file>
  </doc>
  <doc>
    <id>2088</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>26</pageFirst>
    <pageLast>32</pageLast>
    <pageNumber/>
    <edition/>
    <issue>88, S4</issue>
    <volume>2022</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Elsevier</publisherName>
    <publisherPlace>Amsterdam</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-03-18</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Validation of a classification and scoring system for the diagnosis of laryngeal and pharyngeal squamous cell carcinomas by confocal laser endomicroscopy</title>
    <abstract language="eng">Introduction&#13;
Confocal laser endomicroscopy is an optical imaging technique that allows in vivo, real-time, microscope-like images of the upper aerodigestive tract’s mucosa. The assessment of morphological tissue characteristics for the correct differentiation between healthy and malignant suspected mucosa requires strict evaluation criteria.&#13;
&#13;
Objective&#13;
This study aims to validate an eight-point score for the correct assessment of malignancy.&#13;
&#13;
Methods&#13;
We performed confocal laser endomicroscopy between March and October 2020 in 13 patients. 197 sequences (11.820 images) originated from the marginal area of pharyngeal and laryngeal carcinomas. Specimens were taken at corresponding locations and analyzed in H&amp;E staining as a standard of reference. A total of six examiners evaluated the sequences based on a scoring system; they were blinded to the histopathological examination. The primary endpoints are sensitivity, specificity, and accuracy. Secondary endpoints are interrater reliability and receiver operator characteristics.&#13;
&#13;
Results&#13;
Healthy mucosa showed epithelium with uniform size and shape with distinct cytoplasmic membranes and regular vessel architecture. Confocal laser endomicroscopy of malignant cells demonstrated a disorganized arrangement of variable cellular morphology. We calculated an accuracy, sensitivity, specificity, positive predictive value, and negative predictive value of 83.2%, 81.3%, 85.5%, 86.7%, and 79.7%, respectively, with a κ-value of 0.64, and an area under the curve of 0.86.&#13;
&#13;
Conclusion&#13;
The results confirm that this scoring system is applicable in the laryngeal and pharyngeal mucosa to classify benign and malignant tissue. A scoring system based on defined and reproducible characteristics can help translate this experimental method to broad clinical practice in head and neck diagnosis.</abstract>
    <parentTitle language="eng">Brazilian Journal of Otorhinolaryngology</parentTitle>
    <identifier type="issn">1808-8694</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-20887</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1016/j.bjorl.2021.06.002</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Matti</first_name>
      <last_name>Sievert</last_name>
    </author>
    <author>
      <first_name>Konstantinos</first_name>
      <last_name>Mantsopoulos</last_name>
    </author>
    <author>
      <first_name>Sarina K.</first_name>
      <last_name>Müller</last_name>
    </author>
    <author>
      <first_name>Robin</first_name>
      <last_name>Rupp</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Eckstein</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Heinrich</first_name>
      <last_name>Iro</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Goncalves</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>confocal laser endomicroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>squamous cell carcinoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>head and neck cancer</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>diagnosis</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2088/1-s2.0-S1808869421001245-main.pdf</file>
  </doc>
  <doc>
    <id>2319</id>
    <completedYear/>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>2029</pageFirst>
    <pageLast>2037</pageLast>
    <pageNumber/>
    <edition/>
    <issue>4</issue>
    <volume>279</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2022-05-04</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Impact of intraepithelial capillary loops and atypical vessels in confocal laser endomicroscopy for the diagnosis of laryngeal and hypopharyngeal squamous cell carcinoma</title>
    <abstract language="eng">Purpose&#13;
Confocal laser endomicroscopy (CLE) allows surface imaging of the laryngeal and pharyngeal mucosa in vivo at a thousand-fold magnification. This study aims to compare irregular blood vessels and intraepithelial capillary loops in healthy mucosa and squamous cell carcinoma (SCC) via CLE.&#13;
&#13;
Materials and methods&#13;
We included ten patients with confirmed SCC and planned total laryngectomy in this study between March 2020 and February 2021. CLE images of these patients were collected and compared with the corresponding histology in hematoxylin and eosin staining. We analyzed the characteristic endomicroscopic patterns of blood vessels and intraepithelial capillary loops for the diagnosis of SCC.&#13;
&#13;
Results&#13;
In a total of 54 sequences, we identified 243 blood vessels which were analyzed regarding structure, diameter, and Fluorescein leakage, confirming that irregular, corkscrew-like vessels (24.4% vs. 1.3%; P &lt; .001), dilated intraepithelial capillary loops (90.8% vs. 28.7%; P &lt; .001), and increased capillary leakage (40.7% vs. 2.5%; P &lt; .001), are significantly more frequently detected in SCC compared to the healthy epithelium. We defined a vessel diameter of 30 μm in capillary loops as a cut-off value, obtaining a sensitivity, specificity, PPV, and NPV and accuracy of 90.6%, 71.3%, 57.4%, 94.7%, and 77.1%, respectively, for the detection of malignancy based solely on capillary architecture.&#13;
&#13;
Conclusion&#13;
Capillaries within malignant lesions are fundamentally different from those in healthy mucosa regions. The capillary architecture is a significant feature aiding the identification of malignant mucosa areas during in-vivo, real-time CLE examination.</abstract>
    <parentTitle language="eng">European Archives of Oto-Rhino-Laryngology and Head &amp; Neck</parentTitle>
    <identifier type="issn">1434-4726</identifier>
    <identifier type="issn">0937-4477</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-23196</identifier>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/s00405-021-06954-8</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="opus.source">publish</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Matti</first_name>
      <last_name>Sievert</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Eckstein</last_name>
    </author>
    <author>
      <first_name>Konstantinos</first_name>
      <last_name>Mantsopoulos</last_name>
    </author>
    <author>
      <first_name>Sarina K.</first_name>
      <last_name>Müller</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Heinrich</first_name>
      <last_name>Iro</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Goncalves</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>confocal laser endomicroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>head and neck squamous cell carcinoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>capillary loops</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>malignant vascularization</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>head and neck malignancies</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/2319/Article_ImpactOfIntraepithelial.pdf</file>
  </doc>
  <doc>
    <id>1091</id>
    <completedYear>2021</completedYear>
    <publishedYear>2021</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst>4433</pageFirst>
    <pageLast>4439</pageLast>
    <pageNumber/>
    <edition/>
    <issue>11</issue>
    <volume>278</volume>
    <articleNumber/>
    <type>article</type>
    <publisherName>Springer Nature</publisherName>
    <publisherPlace>Cham</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>2021-12-22</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Intraoperative free margins assessment of oropharyngeal squamous cell carcinoma with confocal laser endomicroscopy: a pilot study</title>
    <abstract language="eng">Purpose&#13;
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.&#13;
&#13;
Methods&#13;
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.&#13;
&#13;
Results&#13;
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.&#13;
&#13;
Conclusion&#13;
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.</abstract>
    <parentTitle language="eng">European Archives of Oto-Rhino-Laryngology and Head &amp; Neck</parentTitle>
    <identifier type="issn">1434-4726</identifier>
    <identifier type="issn">0937-4477</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-10914</identifier>
    <enrichment key="opus.source">publish</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/s00405-021-06659-y</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Matti</first_name>
      <last_name>Sievert</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Sarina K.</first_name>
      <last_name>Müller</last_name>
    </author>
    <author>
      <first_name>Markus</first_name>
      <last_name>Eckstein</last_name>
    </author>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Konstantinos</first_name>
      <last_name>Mantsopoulos</last_name>
    </author>
    <author>
      <first_name>Heinrich</first_name>
      <last_name>Iro</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Goncalves</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Confocal laser endomicroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Oropharyngeal squamous cell carcinoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Safe surgical margins</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Head and neck malignancies</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/1091/Sievert2021_Article_IntraoperativeFreeMargins.pdf</file>
  </doc>
  <doc>
    <id>5600</id>
    <completedYear/>
    <publishedYear>2024</publishedYear>
    <thesisYearAccepted/>
    <language>eng</language>
    <pageFirst/>
    <pageLast/>
    <pageNumber>11</pageNumber>
    <edition/>
    <issue>5</issue>
    <volume>28</volume>
    <articleNumber>266</articleNumber>
    <type>article</type>
    <publisherName>Springer</publisherName>
    <publisherPlace>Berlin</publisherPlace>
    <creatingCorporation/>
    <contributingCorporation/>
    <belongsToBibliography>1</belongsToBibliography>
    <completedDate>--</completedDate>
    <publishedDate>--</publishedDate>
    <thesisDateAccepted>--</thesisDateAccepted>
    <title language="eng">Oral mucosa - an examination map for confocal laser endomicroscopy within the oral cavity: an experimental clinical study</title>
    <abstract language="eng">Objectives&#13;
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. &#13;
                &#13;
Materials and methods&#13;
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. &#13;
&#13;
Results&#13;
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. &#13;
&#13;
Conclusion&#13;
A physiological “CLE map” for the entire oral cavity could be created for the first time. &#13;
&#13;
Clinical relevance&#13;
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.</abstract>
    <parentTitle language="eng">Clinical Oral Investigations</parentTitle>
    <identifier type="issn">1436-3771</identifier>
    <identifier type="urn">urn:nbn:de:bvb:573-56000</identifier>
    <enrichment key="opus.import.date">2025-01-24T10:38:31+00:00</enrichment>
    <enrichment key="opus.source">sword</enrichment>
    <enrichment key="opus.import.user">deepgreen</enrichment>
    <enrichment key="opus.import.file">attachment; filename=deposit.zip</enrichment>
    <enrichment key="opus.import.checksum">f9db10e61e3f2f5ce566300bb51366c3</enrichment>
    <enrichment key="THI_openaccess">ja</enrichment>
    <enrichment key="THI_review">peer-review</enrichment>
    <enrichment key="THI_relatedIdentifier">https://doi.org/10.1007/s00784-024-05664-9</enrichment>
    <enrichment key="THI_articleversion">published</enrichment>
    <licence>Creative Commons BY 4.0</licence>
    <author>
      <first_name>Nicolai</first_name>
      <last_name>Oetter</last_name>
    </author>
    <author>
      <first_name>Jonas</first_name>
      <last_name>Pröll</last_name>
    </author>
    <author>
      <first_name>Matti</first_name>
      <last_name>Sievert</last_name>
    </author>
    <author>
      <first_name>Miguel</first_name>
      <last_name>Goncalves</last_name>
    </author>
    <author>
      <first_name>Maximilian</first_name>
      <last_name>Rohde</last_name>
    </author>
    <author>
      <first_name>Christopher-Philipp</first_name>
      <last_name>Nobis</last_name>
    </author>
    <author>
      <first_name>Christian</first_name>
      <last_name>Knipfer</last_name>
    </author>
    <author>
      <first_name>Marc</first_name>
      <last_name>Aubreville</last_name>
    </author>
    <author>
      <first_name>Zhaoya</first_name>
      <last_name>Pan</last_name>
    </author>
    <author>
      <first_name>Katharina</first_name>
      <last_name>Breininger</last_name>
    </author>
    <author>
      <first_name>Andreas</first_name>
      <last_name>Maier</last_name>
    </author>
    <author>
      <first_name>Marco</first_name>
      <last_name>Kesting</last_name>
    </author>
    <author>
      <first_name>Florian</first_name>
      <last_name>Stelzle</last_name>
    </author>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Confocal laser endomicroscopy</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Oral cavity</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Oral carcinoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Optical methods</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Oral cavity squamous cell carcinoma</value>
    </subject>
    <subject>
      <language>eng</language>
      <type>uncontrolled</type>
      <value>Head and neck cancer</value>
    </subject>
    <collection role="open_access" number="">open_access</collection>
    <collection role="institutes" number="19309">Fakultät Informatik</collection>
    <collection role="institutes" number="19379">AImotion Bavaria</collection>
    <collection role="Import" number="deepgreen">DeepGreen</collection>
    <thesisPublisher>Technische Hochschule Ingolstadt</thesisPublisher>
    <file>https://opus4.kobv.de/opus4-haw/files/5600/s00784-024-05664-9.pdf</file>
  </doc>
</export-example>
