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, Erik Schoon, Jacques J.G.H.M. Bergman, Peter H.N. de With

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

1 Citation (Scopus)

Abstract

Volumetric Laser Endomicroscopy (VLE) is a promising balloon-based imaging technique for detecting early neoplasia in Barretts Esophagus. An essential pre-processing step for further processing 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. 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.
LanguageEnglish
Title of host publicationProgress in Biomedical Optics and Imaging
PublisherSPIE
Number of pages7
DOIs
StatePublished - 2019
EventSPIE Medical Imaging 2019 - San Diego, United States
Duration: 16 Feb 201921 Feb 2019
http://spie.org/MI/entireprogram/2019-2-20?print=2&SSO=1

Publication series

NameProceedings of SPIE
PublisherSPIE
Volume10949
ISSN (Print)0277-786X

Conference

ConferenceSPIE Medical Imaging 2019
CountryUnited States
CitySan Diego
Period16/02/1921/02/19
Internet address

Fingerprint

Tissue
Lasers
Balloons
Processing
Signal to noise ratio
Pixels
Imaging techniques

Cite this

van der Putten, J., van der Sommen, F., Struyvenberg, M., de Groof, J., Curvers, W., Schoon, E., ... de With, P. H. N. (2019). Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function. In Progress in Biomedical Optics and Imaging (Proceedings of SPIE; Vol. 10949). SPIE. DOI: 10.1117/12.2512192
van der Putten, Joost ; van der Sommen, Fons ; Struyvenberg, Maarten ; de Groof, Jeroen ; Curvers, Wouter ; Schoon, Erik ; J.G.H.M. Bergman, Jacques ; de With, Peter H.N./ Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function. Progress in Biomedical Optics and Imaging. SPIE, 2019. (Proceedings of SPIE).
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abstract = "Volumetric Laser Endomicroscopy (VLE) is a promising balloon-based imaging technique for detecting early neoplasia in Barretts Esophagus. An essential pre-processing step for further processing 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. 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.",
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van der Putten, J, van der Sommen, F, Struyvenberg, M, de Groof, J, Curvers, W, Schoon, E, J.G.H.M. Bergman, J & de With, PHN 2019, Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function. in Progress in Biomedical Optics and Imaging. Proceedings of SPIE, vol. 10949, SPIE, SPIE Medical Imaging 2019, San Diego, United States, 16/02/19. DOI: 10.1117/12.2512192

Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function. / van der Putten, Joost; van der Sommen, Fons; Struyvenberg, Maarten; de Groof, Jeroen; Curvers, Wouter; Schoon, Erik; J.G.H.M. Bergman, Jacques; de With, Peter H.N.

Progress in Biomedical Optics and Imaging. SPIE, 2019. (Proceedings of SPIE; Vol. 10949).

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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AU - van der Sommen,Fons

AU - Struyvenberg,Maarten

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AB - Volumetric Laser Endomicroscopy (VLE) is a promising balloon-based imaging technique for detecting early neoplasia in Barretts Esophagus. An essential pre-processing step for further processing 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. 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.

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van der Putten J, van der Sommen F, Struyvenberg M, de Groof J, Curvers W, Schoon E et al. Tissue segmentation in volumetric laser endomicroscopy data using FusionNet and a domain-specific loss function. In Progress in Biomedical Optics and Imaging. SPIE. 2019. (Proceedings of SPIE). Available from, DOI: 10.1117/12.2512192