Automated 2D Fetal Brain Segmentation of MR Images Using a Deep U-Net

  • Andrik RampunEmail author
  • Deborah Jarvis
  • Paul Griffiths
  • Paul Armitage
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 12047)


Fetal brain segmentation is a difficult task yet an important step to study brain development in utero. In contrast to adult studies automatic fetal brain extraction remains challenging and has limited research mainly due to arbitrary orientation of the fetus, possible movement and lack of annotated data. This paper presents a deep learning method for 2D fetal brain extraction from Magnetic Resonance Imaging (MRI) data using a convolution neural network inspired from the U-Net architecture [1]. We modified the network to suit our segmentation problem by adding deeper convolutional layers allowing the network to capture finer textural information and using more robust functions to avoid overfitting and to deal with imbalanced foreground (brain) and background (non-brain) samples. Experimental results using 200 normal fetal brains consisting of over 11,000 2D images showed that the proposed method produces Dice and Jaccard coefficients of \(92.8 \pm 6.3\%\) and \(86.7 \pm 7.8\%\), respectively providing a significant improvement over the original U-Net and its variants.


Fetal brain segmentation U-Net Deep learning MRI CNN 


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© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Academic Unit of Radiology, Department of Infection, Immunity and Cardiovascular DiseaseUniversity of SheffieldSheffieldUK

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