Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function

Joost van der Putten; Fons van der Sommen; Maarten Struyvenberg; Jeroen de Groof; Wouter Curvers M.D.; Erik Schoon M.D.; Jacques J.G.H.M. M. Bergman M.D.; Peter H. N. de With

doi:10.1117/12.2512192

15 March 2019 Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function

Joost van der Putten, Fons van der Sommen, Maarten Struyvenberg, Jeroen de Groof, Wouter Curvers M.D., Erik Schoon M.D., Jacques J.G.H.M. M. Bergman M.D., Peter H. N. de With

Author Affiliations +

Proceedings Volume 10949, Medical Imaging 2019: Image Processing; 109492J (2019) https://doi.org/10.1117/12.2512192
Event: SPIE Medical Imaging, 2019, San Diego, California, United States

Abstract

Volumetric Laser Endomicroscopy (VLE) is a promising balloon-based imaging technique for detecting early neoplasia in Barretts Esophagus. Especially Computer Aided Detection (CAD) techniques show great promise compared to medical doctors, who cannot reliably find disease patterns in the noisy VLE signal. However, an essential pre-processing step for the CAD system is tissue segmentation. At present, tissue is segmented manually but is not scalable for the entire VLE scan consisting of 1,200 frames of 4,096 × 2,048 pixels. Furthermore, the current CAD methods cannot use the VLE scans to their full potential, as only a small segment of the esophagus is selected for further processing, while an automated segmentation system results in significantly more available data. This paper explores the possibility of automatically segmenting relevant tissue for VLE scans using FusionNet and a domain-specific loss function. The contribution of this work is threefold. First, we propose a tissue segmentation algorithm for VLE scans. Second, we introduce a weighted ground truth that exploits the signal-to-noise ratio characteristics of the VLE data. Third, we compare our algorithm segmentation against two additional VLE experts. The results show that our algorithm annotations are indistinguishable from the expert annotations and therefore the algorithm can be used as a preprocessing step for further classification of the tissue.

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Joost van der Putten, Fons van der Sommen, Maarten Struyvenberg, Jeroen de Groof, Wouter Curvers M.D., Erik Schoon M.D., Jacques J.G.H.M. M. Bergman M.D., and Peter H. N. de With "Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function", Proc. SPIE 10949, Medical Imaging 2019: Image Processing, 109492J (15 March 2019); https://doi.org/10.1117/12.2512192

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