Automated analysis of confocal laser endomicroscopy images to detect head and neck cancer

Dittberner, Andreas; Rodner, Erik; Ortmann, Wolfgang; Stadler, Joachim; Schmidt, Carsten; Petersen, Iver; Stallmach, Andreas; Denzler, Joachim; Guntinas‐Lichius, Orlando

doi:10.1002/hed.24253

Cited by 30 publications

(31 citation statements)

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“…Especially with regard to a possible application during a resection of tumors, where blood, tissue debris, and coagulation artifacts might reduce the contrast even further, a high contrast would be mandatory. It would be worthwhile to develop automated image analysis methods for ICG images as it has also ready been introduced for confocal laser endomicroscopy images . For optical image‐guided surgery, it would be helpful to develop tools to overlay ICG image and white light image .…”

Section: Discussionmentioning

confidence: 99%

“…It would be worthwhile to develop automated image analysis methods for ICG images as it has also ready been introduced for confocal laser endomicroscopy images. 29 For optical image-guided surgery, it would be helpful to develop tools to overlay ICG image and white light image. 30 Finally, the approval of tumor-specific NIR probes or the conjunction of tumor-specific antibodies with unspecific NIR probes will be the most important step to develop intraoperative image-guided cancer surgery.…”

Section: Discussionmentioning

confidence: 99%

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Feasibility of real‐time near‐infrared indocyanine green fluorescence endoscopy for the evaluation of mucosal head and neck lesions

et al. 2016

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BackgroundThe purpose of this study was to explore the feasibility and potential drawbacks of near‐infrared (NIR) endoscopy with indocyanine green (ICG) to examine mucosal head and neck lesions.MethodsNIR ICG endoscopy was applied to image head and neck cancer epithelium in vivo. The evaluation of the ICG videos was performed off‐line independently by 2 evaluators and blinded with respect to final histopathological results from biopsies taken as the gold standard.ResultsForty percent of the lesions from 55 patients were histologically malignant. ICG positivity showed a sensitivity, specificity, and accuracy to be related to a malignant tumor of 90.5%, 90.9%, and 89.1%, respectively. The kappa index for the interobserver assessment showed a 94.4% agreement for the assessment of the ICG positivity. Side effects of the NIR ICG endoscopy did not arise.ConclusionNIR ICG endoscopy in patients with mucosal head and neck lesions was feasible and safe. It might help intraoperatively to differentiate benign from malignant lesions. © 2016 Wiley Periodicals, Inc. Head Neck 39: 234–240, 2017

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Feasibility of real‐time near‐infrared indocyanine green fluorescence endoscopy for the evaluation of mucosal head and neck lesions

et al. 2016

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“…Of course, the oncologic surgeon should know the fundamental principles of optical imaging to understand which conclusions can be drawn from the images . Nevertheless, the head and neck surgeon is not trained to interpret the histology‐like images of tissues in vivo . The pathologist is the natural partner to interpret multimodal nonlinear microscopy images.…”

Section: Discussionmentioning

confidence: 99%

Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study

et al. 2018

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Background: A fully convolutional neural networks (FCN)-based automated image analysis algorithm to discriminate between head and neck cancer and noncancerous epithelium based on nonlinear microscopic images was developed. Methods: Head and neck cancer sections were used for standard histopathology and co-registered with multimodal images from the same sections using the combination of coherent anti-Stokes Raman scattering, two-photon excited fluorescence, and second harmonic generation microscopy. The images analyzed with semantic segmentation using a FCN for four classes: cancer, normal epithelium, background, and other tissue types. Results: A total of 114 images of 12 patients were analyzed. Using a patch score aggregation, the average recognition rate and an overall recognition rate or the four classes were 88.9% and 86.7%, respectively. A total of 113 seconds were needed to process a whole-slice image in the dataset. Conclusion: Multimodal nonlinear microscopy in combination with automated image analysis using FCN seems to be a promising technique for objective differentiation between head and neck cancer and noncancerous epithelium.coherent anti-stokes Raman scattering, convolutional neural networks, diagnostics, digital pathology, head and neck cancer, image analysis, second-harmonic generation, semantic segmentation, spectral histopathology, two-photon excited fluorescence

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“…Außerdem ist die Ausdehnung des Tumors mit der dysplastischen Umgebung besser beurteilbar [43]. [16,44].…”

Section: Merke Die Kombination Des Optischen Filters Bei Der Hohenunclassified

Endoskopische Frühdiagnostik von Karzinomen im oberen Aerodigestivtrakt

Davaris¹,

Voigt-Zimmermann²,

Cyran³

et al. 2019

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Automated analysis of confocal laser endomicroscopy images to detect head and neck cancer

Abstract: The proposed software allows an objective classification of CLE images of head and neck cancer and adjacent nonneoplastic epithelium. © 2015 Wiley Periodicals, Inc. Head Neck 38: E1419-E1426, 2016.

Cited by 30 publications

References 39 publications

Feasibility of real‐time near‐infrared indocyanine green fluorescence endoscopy for the evaluation of mucosal head and neck lesions

Feasibility of real‐time near‐infrared indocyanine green fluorescence endoscopy for the evaluation of mucosal head and neck lesions

Fully convolutional networks in multimodal nonlinear microscopy images for automated detection of head and neck carcinoma: Pilot study

Endoskopische Frühdiagnostik von Karzinomen im oberen Aerodigestivtrakt

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