Automatic Classification and Pathological Staging of Confocal Laser Endomicroscopic Images of the Vocal Cords

Vo, Kim; Jaremenko, Christian; Bohr, Christopher; Neumann, Helmut; Maier, Andreas

doi:10.1007/978-3-662-54345-0_70

Cited by 10 publications

(22 citation statements)

References 2 publications

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“…After this cleaning step, the total number of images of this data set is 4,425. Vo et al have done previous investigations on this data set, and found accuracies in grading of between 86.4% and 89.8% 16 using a patch-based method where they found best results for a support vector machine classifier with a feature set based on gray-level co-occurrence matrices. 15…”

Section: Vocal Cords (Vc)mentioning

confidence: 86%

“…15 Using the same tool chain, Vo et al showed that this detection methodology is also applicable to the detection of SCC of the vocal folds. 16 On a larger (and thus more realistic) data set of OSCC, the performance of these algorithms decreased, however. 17 We were able to show, that the usage of a CNN for classification of extracted patches greatly improves performance.…”

Section: Related Workmentioning

confidence: 99%

“…2). Because of this, besides patch-based methods 15,16 that extract rectangular patches from the round image area, all image-scale classification approaches known today use squared center crop of the overall image, which reduces the available data for classification. 17,18,20 Since the magnification of pCLE is very high and matching with histology images is almost impossible, image segmentation data of malignancy is hard to derive for the expert, so data sets typically have only image labels.…”

Section: Related Workmentioning

confidence: 99%

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Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract

Aubreville

Goncalves

Knipfer

et al. 2019

Biomedical Engineering Systems and Technologies

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Squamous Cell Carcinoma (SCC) is the most common cancer type of the epithelium and is often detected at a late stage. Besides invasive diagnosis of SCC by means of biopsy and histo-pathologic assessment, Confocal Laser Endomicroscopy (CLE) has emerged as noninvasive method that was successfully used to diagnose SCC in vivo. For interpretation of CLE images, however, extensive training is required, which limits its applicability and use in clinical practice of the method.To aid diagnosis of SCC in a broader scope, automatic detection methods have been proposed. This work compares two methods with regard to their applicability in a transfer learning sense, i.e. training on one tissue type (from one clinical team) and applying the learnt classification system to another entity (different anatomy, different clinical team). Besides a previously proposed, patch-based method based on convolutional neural networks, a novel classification method on image level (based on a pre-trained Inception V.3 network with dedicated preprocessing and interpretation of class activation maps) is proposed and evaluated.The newly presented approach improves recognition performance, yielding accuracies of 91.63% on the first data set (oral cavity) and 92.63% on a joint data set. The generalization from oral cavity to the second data set (vocal folds) lead to similar area-under-the-ROC curve values than a direct training on the vocal folds data set, indicating good generalization.

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Section: Vocal Cords (Vc)mentioning

confidence: 86%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

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Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract

Aubreville

Goncalves

Knipfer

et al. 2019

Biomedical Engineering Systems and Technologies

Self Cite

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“…(eCLE) (Boschetto et al, 2016a) Oropharyngeal Pathological epithelium. Jaremenko et al, 2015;Vo et al, 2017) Brain Brain tumours (glioma and meningioma). (Kamen et al, 2016;Wan et al, 2015) Ovaries Epithelial changes (Srivastava et al, 2005;Srivastava et al, 2008)…”

Section: Gastrointestinalmentioning

confidence: 99%

“…Oriented FAST and rotated BRIEF (ORB) (Wan et al, 2015), (vi) Histogram of Oriented Gradients (HOG) (Gu et al, 2016;Vo et al, 2017), (vii) textons (Gu et al, 2016), (viii) Local Derivative Patterns (LDP) (Vo et al, 2017), as well as (ix) features extracted from Convolutional Neural Networks (CNN) prior to the fully connected layer employed for computing each class score (Gil et al, 2017;Vo et al, 2017). (Leonovych et al, 2018) introduced Sparse Irregular Local Binary Patterns (SILBP), an adaptation of LBPs taking into consideration the sparse, irregular sampling imposed by the imaging fibre bundle on FBEµ images.…”

Section: Image Classificationmentioning

confidence: 99%

Image computing for fibre-bundle endomicroscopy: A review

Perperidis¹,

Dhaliwal²,

McLaughlin³

et al. 2020

Medical Image Analysis

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Endomicroscopy is an emerging imaging modality, that facilitates the acquisition of in vivo, in situ optical biopsies, assisting diagnostic and potentially therapeutic interventions. While there is a diverse and constantly expanding range of commercial and experimental optical biopsy platforms available, fibre-bundle endomicroscopy is currently the most widely used platform and is approved for clinical use in a range of clinical indications. Miniaturised, flexible fibre-bundles, guided through the working channel of endoscopes, needles and catheters, enable high-resolution imaging across a variety of organ systems. Yet, the nature of image acquisition though a fibre-bundle gives rise to several inherent characteristics and limitations necessitating novel and effective image pre-and post-processing algorithms, ranging from image formation, enhancement and mosaicing to pathology detection and quantification. This paper introduces the underlying technology and most prevalent clinical applications of fibre-bundle endomicroscopy, and provides a comprehensive, up-to-date, review of relevant image reconstruction, analysis and understanding/inference methodologies. Furthermore, current limitations as well as future challenges and opportunities in fibre-bundle endomicroscopy computing are identified and discussed.

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Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images

et al. 2018

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Automatic Classification and Pathological Staging of Confocal Laser Endomicroscopic Images of the Vocal Cords

Cited by 10 publications

References 2 publications

Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract

Transferability of Deep Learning Algorithms for Malignancy Detection in Confocal Laser Endomicroscopy Images from Different Anatomical Locations of the Upper Gastrointestinal Tract

Image computing for fibre-bundle endomicroscopy: A review

Deep learning-based detection of motion artifacts in probe-based confocal laser endomicroscopy images

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