Advertisement

GTCreator: a flexible annotation tool for image-based datasets

  • Jorge BernalEmail author
  • Aymeric Histace
  • Marc Masana
  • Quentin Angermann
  • Cristina Sánchez-Montes
  • Cristina Rodríguez de Miguel
  • Maroua Hammami
  • Ana García-Rodríguez
  • Henry Córdova
  • Olivier Romain
  • Gloria Fernández-Esparrach
  • Xavier Dray
  • F. Javier Sánchez
Original Article

Abstract

Purpose:

Methodology evaluation for decision support systems for health is a time-consuming task. To assess performance of polyp detection methods in colonoscopy videos, clinicians have to deal with the annotation of thousands of images. Current existing tools could be improved in terms of flexibility and ease of use.

Methods:

We introduce GTCreator, a flexible annotation tool for providing image and text annotations to image-based datasets. It keeps the main basic functionalities of other similar tools while extending other capabilities such as allowing multiple annotators to work simultaneously on the same task or enhanced dataset browsing and easy annotation transfer aiming to speed up annotation processes in large datasets.

Results:

The comparison with other similar tools shows that GTCreator allows to obtain fast and precise annotation of image datasets, being the only one which offers full annotation editing and browsing capabilites.

Conclusion:

Our proposed annotation tool has been proven to be efficient for large image dataset annotation, as well as showing potential of use in other stages of method evaluation such as experimental setup or results analysis.

Keywords

Annotation tool Validation framework Benchmark Colonoscopy Evaluation 

Notes

Funding

This work has been funded by Spanish Government through iVENDIS (DPI2015-65286-R), DeepMTL (TIN2016-79717-R) and HISINVIA(PI17/00894) projects, Catalan government through SGR-2017-1669 , SGR-2017-653 and CERCA programme, Région Île de France through SATT funding “iPolyp” (Project 184). A. Histace and J. Bernal acknowledge the Institute of Advanced Studies from UCP (Invited Prof. Position grant) as well as Initiative Paris Seine through which the position was obtained in the context of “iPolyp”. M. Masana acknowledges 2017FIB-00218 grant of Generalitat de Catalunya. We also acknowledge the generous GPU support from NVIDIA.

Compliances with ethical standards

Conflict of interest

The authors declare that they have no conflict of interest.

Ethical approval

All procedures performed in studies involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki Declaration and its later amendments or comparable ethical standards.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Supplementary material

11548_2018_1864_MOESM1_ESM.mp4 (20.6 mb)
Supplementary material 1 (mp4 21101 KB)

References

  1. 1.
    El-Regaily SA, Salem MA, Abdel Aziz MH, Roushdy MI (2018) Survey of computer aided detection systems for lung cancer in computed tomography. Curr Med Imaging Rev 14(1):3–18CrossRefGoogle Scholar
  2. 2.
    Gui L, Yang X (2018) Automatic renal lesion segmentation in ultrasound images based on saliency features, improved lbp, and an edge indicator under level set framework. Med Phys 45(1):223–235CrossRefGoogle Scholar
  3. 3.
    Sánchez FJ, Bernal J, Sánchez-Montes C, de Miguel CR, Fernández-Esparrach G (2017) Bright spot regions segmentation and classification for specular highlights detection in colonoscopy videos. Mach Vis Appl 28(8):917–936CrossRefGoogle Scholar
  4. 4.
    Mori Y, Kudo Se, Berzin TM, Misawa M, Takeda K (2017) Computer-aided diagnosis for colonoscopy. Endoscopy 49(08):813–819CrossRefGoogle Scholar
  5. 5.
    Bernal J, Tajkbaksh N, Snchez FJ, Matuszewski BJ, Chen H, Yu L, Angermann Q, Romain O, Rustad B, Balasingham I, Pogorelov K, Choi S, Debard Q, Maier-Hein L, Speidel S, Stoyanov D, Brandao P, Crdova H, Snchez-Montes C, Gurudu SR, Fernndez-Esparrach G, Dray X, Liang J, Histace A (2017) Comparative validation of polyp detection methods in video colonoscopy: results from the miccai 2015 endoscopic vision challenge. IEEE Trans Med Imaging 36(6):1231–1249CrossRefGoogle Scholar
  6. 6.
    Chen PJ, Lin MC, Lai MJ, Lin JC, Lu HHS, Tseng VS (2018) Accurate classification of diminutive colorectal polyps using computer-aided analysis. Gastroenterology 154(3):568–575CrossRefGoogle Scholar
  7. 7.
    Russell BC, Torralba A, Murphy KP, Freeman WT (2008) Labelme: a database and web-based tool for image annotation. Int J Comput Vis 77(1):157–173CrossRefGoogle Scholar
  8. 8.
    Dutta A, Gupta A, Zissermann A (2016) VGG image annotator (VIA). http://www.robots.ox.ac.uk/~vgg/software/via/. Accessed 21 Jan 2018
  9. 9.
    Iakovidis D, Goudas T, Smailis C, Maglogiannis I (2014) Ratsnake: a versatile image annotation tool with application to computer-aided diagnosis. Sci World J 2014:1–12Google Scholar
  10. 10.
    Liu D, Cao Y, Kim KH, Stanek S, Doungratanaex-Chai B, Lin K, Tavanapong W, Wong J, Oh J, De Groen PC (2007) Arthemis: annotation software in an integrated capturing and analysis system for colonoscopy. Comput Methods Programs Biomed 88(2):152–163CrossRefGoogle Scholar
  11. 11.
    Rubin DL, Rodriguez C, Shah P, Beaulieu C (2008) ipad: semantic annotation and markup of radiological images. In: AMIA annual symposium proceedings, vol 2008. American Medical Informatics Association, p 626Google Scholar
  12. 12.
    Several authors (2013) Video image annotation tool. https://sourceforge.net/projects/via-tool/. Accessed 22 Jan 2018
  13. 13.
    Abràmoff MD, Magalhães PJ, Ram SJ (2004) Image processing with imagej. Biophoton Int 11(7):36–42Google Scholar

Copyright information

© CARS 2018

Authors and Affiliations

  1. 1.Computer Vision Center and Universitat Autonòma de BarcelonaBarcelonaSpain
  2. 2.ETIS lab, ENSEA, CNRSUniversity of Cergy-PontoiseCergyFrance
  3. 3.Endoscopy Unit, ICMDiM, Hospital Clnic, IDIBAPS, CIBEREHDUniversity of BarcelonaBarcelonaSpain
  4. 4.St. Antoine HospitalAPHPParisFrance

Personalised recommendations