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Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer

Part of the Lecture Notes in Computer Science book series (LNIP,volume 10550)

Abstract

Confocal Laser Endomicroscopy (CLE) is an emerging imaging technique that allows the in-vivo acquisition of cell patterns of potentially malignant lesions. Such patterns could discriminate between inflammatory and neoplastic lesions and, thus, serve as a first in-vivo biopsy to discard cases that do not actually require a cell biopsy.

The goal of this work is to explore whether CLE images obtained during videobronchoscopy contain enough visual information to discriminate between benign and malign peripheral lesions for lung cancer diagnosis. To do so, we have performed a pilot comparative study with 12 patients (6 adenocarcinoma and 6 benign-inflammatory) using 2 different methods for CLE pattern analysis: visual analysis by 3 experts and a novel methodology that uses graph methods to find patterns in pre-trained feature spaces. Our preliminary results indicate that although visual analysis can only achieve a 60.2% of accuracy, the accuracy of the proposed unsupervised image pattern classification raises to 84.6%.

We conclude that CLE images visual information allow in-vivo detection of neoplastic lesions and graph structural analysis applied to deep-learning feature spaces can achieve competitive results.

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Acknowledgments

Work supported by projects DPI2015-65286-R, FIS-ETES PI09/90917, 2014-SGR-1470 and Fundació Marató TV3 20133510. Also supported by CERCA Programme/Generalitat de Catalunya. The Titan X Pascal used for this research was donated by the NVIDIA Corporation. Finally, Debora Gil is supported by the Serra Hunter Program.

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Correspondence to Carles Sanchez .

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Gil, D. et al. (2017). Classification of Confocal Endomicroscopy Patterns for Diagnosis of Lung Cancer. In: , et al. Computer Assisted and Robotic Endoscopy and Clinical Image-Based Procedures. CARE CLIP 2017 2017. Lecture Notes in Computer Science(), vol 10550. Springer, Cham. https://doi.org/10.1007/978-3-319-67543-5_15

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  • DOI: https://doi.org/10.1007/978-3-319-67543-5_15

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