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Esmaeili N, Davaris N, Boese A, Illanes A, Navab N, Friebe M, Arens C. Contact Endoscopy - Narrow Band Imaging (CE-NBI) data set for laryngeal lesion assessment. Sci Data 2023; 10:733. [PMID: 37865668 PMCID: PMC10590430 DOI: 10.1038/s41597-023-02629-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/17/2022] [Accepted: 10/11/2023] [Indexed: 10/23/2023] Open
Abstract
The endoscopic examination of subepithelial vascular patterns within the vocal fold is crucial for clinicians seeking to distinguish between benign lesions and laryngeal cancer. Among innovative techniques, Contact Endoscopy combined with Narrow Band Imaging (CE-NBI) offers real-time visualization of these vascular structures. Despite the advent of CE-NBI, concerns have arisen regarding the subjective interpretation of its images. As a result, several computer-based solutions have been developed to address this issue. This study introduces the CE-NBI data set, the first publicly accessible data set that features enhanced and magnified visualizations of subepithelial blood vessels within the vocal fold. This data set encompasses 11144 images from 210 adult patients with pathological vocal fold conditions, where CE-NBI images are annotated using three distinct label categories. The data set has proven invaluable for numerous clinical assessments geared toward diagnosing laryngeal cancer using Optical Biopsy. Furthermore, given its versatility for various image analysis tasks, we have devised and implemented diverse image classification scenarios using Machine Learning (ML) approaches to address critical clinical challenges in assessing laryngeal lesions.
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Affiliation(s)
- Nazila Esmaeili
- Department of Otorhinolaryngology, Head and Neck Surgery, Justus Liebig University of Giessen, 35392, Giessen, Germany.
- Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, 85748, Munich, Germany.
- SURAG Medical GmbH, 04103, Leipzig, Germany.
| | - Nikolaos Davaris
- Department of Otorhinolaryngology, Head and Neck Surgery, Giessen University Hospital, 35392, Giessen, Germany
- Department of Otorhinolaryngology, Head and Neck Surgery, Magdeburg University Hospital, 39120, Magdeburg, Germany
| | - Axel Boese
- INKA-Innovation Laboratory for Image Guided Therapy, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
| | | | - Nassir Navab
- Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, 85748, Munich, Germany
| | - Michael Friebe
- INKA-Innovation Laboratory for Image Guided Therapy, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120, Magdeburg, Germany
- Department of Biocybernetics and Biomedical Engineering, AGH University Kraków, 30-059, Kraków, Poland
- CIBE - Center for Innovation, Business Development & Entrepreneurship, FOM University of Applied Sciences, 45141, Essen, Germany
| | - Christoph Arens
- Department of Otorhinolaryngology, Head and Neck Surgery, Giessen University Hospital, 35392, Giessen, Germany
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Esmaeili N, Sharaf E, Gomes Ataide EJ, Illanes A, Boese A, Davaris N, Arens C, Navab N, Friebe M. Deep Convolution Neural Network for Laryngeal Cancer Classification on Contact Endoscopy-Narrow Band Imaging. SENSORS 2021; 21:s21238157. [PMID: 34884166 PMCID: PMC8662427 DOI: 10.3390/s21238157] [Citation(s) in RCA: 7] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 10/31/2021] [Revised: 12/02/2021] [Accepted: 12/03/2021] [Indexed: 12/14/2022]
Abstract
(1) Background: Contact Endoscopy (CE) and Narrow Band Imaging (NBI) are optical imaging modalities that can provide enhanced and magnified visualization of the superficial vascular networks in the laryngeal mucosa. The similarity of vascular structures between benign and malignant lesions causes a challenge in the visual assessment of CE-NBI images. The main objective of this study is to use Deep Convolutional Neural Networks (DCNN) for the automatic classification of CE-NBI images into benign and malignant groups with minimal human intervention. (2) Methods: A pretrained Res-Net50 model combined with the cut-off-layer technique was selected as the DCNN architecture. A dataset of 8181 CE-NBI images was used during the fine-tuning process in three experiments where several models were generated and validated. The accuracy, sensitivity, and specificity were calculated as the performance metrics in each validation and testing scenario. (3) Results: Out of a total of 72 trained and tested models in all experiments, Model 5 showed high performance. This model is considerably smaller than the full ResNet50 architecture and achieved the testing accuracy of 0.835 on the unseen data during the last experiment. (4) Conclusion: The proposed fine-tuned ResNet50 model showed a high performance to classify CE-NBI images into the benign and malignant groups and has the potential to be part of an assisted system for automatic laryngeal cancer detection.
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Affiliation(s)
- Nazila Esmaeili
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
- Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, 85748 Munich, Germany;
- Correspondence:
| | - Esam Sharaf
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
| | - Elmer Jeto Gomes Ataide
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
- Department of Nuclear Medicine, Medical Faculty, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany
| | - Alfredo Illanes
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
| | - Axel Boese
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
| | - Nikolaos Davaris
- Department of Otorhinolaryngology, Head and Neck Surgery, Magdeburg University Hospital, 39120 Magdeburg, Germany;
| | - Christoph Arens
- Department of Otorhinolaryngology, Head and Neck Surgery, Giessen University Hospital, 35392 Giessen, Germany;
| | - Nassir Navab
- Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich, 85748 Munich, Germany;
| | - Michael Friebe
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (E.S.); (E.J.G.A.); (A.I.); (A.B.); (M.F.)
- IDTM GmbH, 45657 Recklinghausen, Germany
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Esmaeili N, Boese A, Davaris N, Arens C, Navab N, Friebe M, Illanes A. Cyclist Effort Features: A Novel Technique for Image Texture Characterization Applied to Larynx Cancer Classification in Contact Endoscopy-Narrow Band Imaging. Diagnostics (Basel) 2021; 11:432. [PMID: 33802625 PMCID: PMC8001098 DOI: 10.3390/diagnostics11030432] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/29/2021] [Revised: 02/24/2021] [Accepted: 02/26/2021] [Indexed: 11/20/2022] Open
Abstract
BACKGROUND Feature extraction is an essential part of a Computer-Aided Diagnosis (CAD) system. It is usually preceded by a pre-processing step and followed by image classification. Usually, a large number of features is needed to end up with the desired classification results. In this work, we propose a novel approach for texture feature extraction. This method was tested on larynx Contact Endoscopy (CE)-Narrow Band Imaging (NBI) image classification to provide more objective information for otolaryngologists regarding the stage of the laryngeal cancer. METHODS The main idea of the proposed methods is to represent an image as a hilly surface, where different paths can be identified between a starting and an ending point. Each of these paths can be thought of as a Tour de France stage profile where a cyclist needs to perform a specific effort to arrive at the finish line. Several paths can be generated in an image where different cyclists produce an average cyclist effort representing important textural characteristics of the image. Energy and power as two Cyclist Effort Features (CyEfF) were extracted using this concept. The performance of the proposed features was evaluated for the classification of 2701 CE-NBI images into benign and malignant lesions using four supervised classifiers and subsequently compared with the performance of 24 Geometrical Features (GF) and 13 Entropy Features (EF). RESULTS The CyEfF features showed maximum classification accuracy of 0.882 and improved the GF classification accuracy by 3 to 12 percent. Moreover, CyEfF features were ranked as the top 10 features along with some features from GF set in two feature ranking methods. CONCLUSION The results prove that CyEfF with only two features can describe the textural characterization of CE-NBI images and can be part of the CAD system in combination with GF for laryngeal cancer diagnosis.
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Affiliation(s)
- Nazila Esmaeili
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (A.B.); (M.F.); (A.I.)
- Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University Munich, 85748 Munich, Germany;
| | - Axel Boese
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (A.B.); (M.F.); (A.I.)
| | - Nikolaos Davaris
- Department of Otorhinolaryngology, Head and Neck Surgery, Magdeburg University Hospital, 39120 Magdeburg, Germany; (N.D.); (C.A.)
| | - Christoph Arens
- Department of Otorhinolaryngology, Head and Neck Surgery, Magdeburg University Hospital, 39120 Magdeburg, Germany; (N.D.); (C.A.)
| | - Nassir Navab
- Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University Munich, 85748 Munich, Germany;
| | - Michael Friebe
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (A.B.); (M.F.); (A.I.)
- IDTM GmbH, 45657 Recklinghausen, Germany
| | - Alfredo Illanes
- INKA—Innovation Laboratory for Image Guided Therapy, Otto-von-Guericke University Magdeburg, 39120 Magdeburg, Germany; (A.B.); (M.F.); (A.I.)
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Laryngeal Lesion Classification Based on Vascular Patterns in Contact Endoscopy and Narrow Band Imaging: Manual Versus Automatic Approach. SENSORS 2020; 20:s20144018. [PMID: 32707740 PMCID: PMC7411577 DOI: 10.3390/s20144018] [Citation(s) in RCA: 12] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 05/29/2020] [Revised: 07/17/2020] [Accepted: 07/18/2020] [Indexed: 12/20/2022]
Abstract
Longitudinal and perpendicular changes in the vocal fold’s blood vessels are associated with the development of benign and malignant laryngeal lesions. The combination of Contact Endoscopy (CE) and Narrow Band Imaging (NBI) can provide intraoperative real-time visualization of the vascular changes in the laryngeal mucosa. However, the visual evaluation of vascular patterns in CE-NBI images is challenging and highly depends on the clinicians’ experience. The current study aims to evaluate and compare the performance of a manual and an automatic approach for laryngeal lesion’s classification based on vascular patterns in CE-NBI images. In the manual approach, six observers visually evaluated a series of CE+NBI images that belong to a patient and then classified the patient as benign or malignant. For the automatic classification, an algorithm based on characterizing the level of the vessel’s disorder in combination with four supervised classifiers was used to classify CE-NBI images. The results showed that the manual approach’s subjective evaluation could be reduced by using a computer-based approach. Moreover, the automatic approach showed the potential to work as an assistant system in case of disagreements among clinicians and to reduce the manual approach’s misclassification issue.
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