Evaluate Fine-tuning Strategies for Fetal Head Ultrasound Image
  Segmentation with U-Net
Fangyijie Wang Guénolé Silvestre Kathleen M. Curran
Fetal head segmentation is a crucial step in measuring the fetal head
circumference (HC) during gestation, an important biometric in obstetrics for
monitoring fetal growth. However, manual biometry generation is time-consuming
and results in inconsistent accuracy. To address this issue, convolutional
neural network (CNN) models have been utilized to improve the efficiency of
medical biometry. But training a CNN network from scratch is a challenging
task, we proposed a Transfer Learning (TL) method. Our approach involves
fine-tuning (FT) a U-Net network with a lightweight MobileNet as the encoder to
perform segmentation on a set of fetal head ultrasound (US) images with limited
effort. This method addresses the challenges associated with training a CNN
network from scratch. It suggests that our proposed FT strategy yields
segmentation performance that is comparable when trained with a reduced number
of parameters by 85.8%. And our proposed FT strategy outperforms other
strategies with smaller trainable parameter sizes below 4.4 million. Thus, we
contend that it can serve as a dependable FT approach for reducing the size of
models in medical image analysis. Our key findings highlight the importance of
the balance between model performance and size in developing Artificial
Intelligence (AI) applications by TL methods. Code is available at
https://github.com/13204942/FT_Methods_for_Fetal_Head_Segmentation.