Deep Learning Approaches for Gynaecological Ultrasound Image Segmentation: A Radio-Frequency vs B-mode Comparison

  • Catarina CarvalhoEmail author
  • Sónia Marques
  • Carla Peixoto
  • Duarte Pignatelli
  • Jorge Beires
  • Jorge Silva
  • Aurélio Campilho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 11663)


Ovarian cancer is one of the pathologies with the worst prognostic in adult women and it has a very difficult early diagnosis. Clinical evaluation of gynaecological ultrasound images is performed visually, and it is dependent on the experience of the medical doctor. Besides the dependency on the specialists, the malignancy of specific types of ovarian tumors cannot be asserted until their surgical removal. This work explores the use of ultrasound data for the segmentation of the ovary and the ovarian follicles, using two different convolutional neural networks, a fully connected residual network and a U-Net, with a binary and multi-class approach. Five different types of ultrasound data (from beam-formed radio-frequency to brightness mode) were used as input. The best performance was obtained using B-mode, for both ovary and follicles segmentation. No significant differences were found between the two convolutional neural networks. The use of the multi-class approach was beneficial as it provided the model information on the spatial relation between follicles and the ovary. This study demonstrates the suitability of combining convolutional neural networks with beam-formed radio-frequency data and with brightness mode data for segmentation of ovarian structures. Future steps involve the processing of pathological data and investigation of biomarkers of pathological ovaries.


B-mode ultrasound data Beam-formed ultrasound data Image segmentation Neuronal networks Ovarian cancer 



This work is financed by National Funds through the Portuguese funding agency, FCT - Fundação para a Ciência e a Tecnologia as part of project “UID/EEA/50014/2019”.


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Copyright information

© Springer Nature Switzerland AG 2019

Authors and Affiliations

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

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