Abstract
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.
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Rampun, A., Jarvis, D., Griffiths, P., Armitage, P. (2020). Automated 2D Fetal Brain Segmentation of MR Images Using a Deep U-Net. In: Palaiahnakote, S., Sanniti di Baja, G., Wang, L., Yan, W. (eds) Pattern Recognition. ACPR 2019. Lecture Notes in Computer Science(), vol 12047. Springer, Cham. https://doi.org/10.1007/978-3-030-41299-9_29
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