  Accurate measurement of fetal head circumference is crucial for estimating
fetal growth during routine prenatal screening. Prior to measurement, it is
necessary to accurately identify and segment the region of interest,
specifically the fetal head, in ultrasound images. Recent advancements in deep
learning techniques have shown significant progress in segmenting the fetal
head using encoder-decoder models. Among these models, U-Net has become a
standard approach for accurate segmentation. However, training an
encoder-decoder model can be a time-consuming process that demands substantial
computational resources. Moreover, fine-tuning these models is particularly
challenging when there is a limited amount of data available. There are still
no "best-practice" guidelines for optimal fine-tuning of U-net for fetal
ultrasound image segmentation. This work summarizes existing fine-tuning
strategies with various backbone architectures, model components, and
fine-tuning strategies across ultrasound data from Netherlands, Spain, Malawi,
Egypt and Algeria. Our study shows that (1) fine-tuning U-Net leads to better
performance than training from scratch, (2) fine-tuning strategies in decoder
are superior to other strategies, (3) network architecture with less number of
parameters can achieve similar or better performance. We also demonstrate the
effectiveness of fine-tuning strategies in low-resource settings and further
expand our experiments into few-shot learning. Lastly, we publicly released our
code and specific fine-tuned weights.
