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End-to-End Ovarian Structures Segmentation

  • Diego S. WanderleyEmail author
  • Catarina B. Carvalho
  • Ana Domingues
  • Carla Peixoto
  • Duarte Pignatelli
  • Jorge Beires
  • Jorge Silva
  • Aurélio Campilho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11401)

Abstract

The segmentation and characterization of the ovarian structures are important tasks in gynecological and reproductive medicine. Ultrasound imaging is typically used for the medical diagnosis within this field but the understanding of the images can be difficult due to their characteristics. Furthermore, the complexity of ultrasound data may lead to a heavy image processing, which makes the application of classical methods of computer vision difficult. This work presents the first supervised fully convolutional neural network (fCNN) for the automatic segmentation of ovarian structures in B-mode ultrasound images. Due to the small dataset available, only 57 images were used for training. In order to overcome this limitation, several regularization techniques were used and are discussed in this paper. The experiments show the ability of the fCNN to learn features to distinguish ovarian structures, achieving a Dice similarity coefficient (DSC) of 0.855 for the segmentation of the stroma and a DSC of 0.955 for the follicles. When compared with a semi-automatic commercial application for follicle segmentation, the proposed fCNN achieved an average improvement of 19%.

Keywords

Ovarian structures segmentation Ultrasound imaging Convolutional neural network (CNN) 

Notes

Acknowledgments

This work is financed by the ERDF – European Regional Development Fund through the Operational Programme for Competitiveness and Internationalisation - COMPETE 2020 Programme within project “POCI-01-0145-FEDER-006961”, and by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia as part of project “UID/EEA/50014/2013”.

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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

  • Diego S. Wanderley
    • 1
    • 2
    Email author
  • Catarina B. Carvalho
    • 2
  • Ana Domingues
    • 2
  • Carla Peixoto
    • 3
  • Duarte Pignatelli
    • 3
    • 4
  • Jorge Beires
    • 3
  • Jorge Silva
    • 1
    • 2
  • Aurélio Campilho
    • 1
    • 2
  1. 1.Faculdade de Engenharia da Universidade do PortoPortoPortugal
  2. 2.INESC TECPortoPortugal
  3. 3.Centro Hospitalar de São JoãoPortoPortugal
  4. 4.Faculdade de Medicina da Universidade do PortoPortoPortugal

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