  Ultrasound (US) is one of the most commonly used imaging modalities in both
diagnosis and surgical interventions due to its low-cost, safety, and
non-invasive characteristic. US image segmentation is currently a unique
challenge because of the presence of speckle noise. As manual segmentation
requires considerable efforts and time, the development of automatic
segmentation algorithms has attracted researchers attention. Although recent
methodologies based on convolutional neural networks have shown promising
performances, their success relies on the availability of a large number of
training data, which is prohibitively difficult for many applications.
Therefore, in this study we propose the use of simulated US images and natural
images as auxiliary datasets in order to pre-train our segmentation network,
and then to fine-tune with limited in vivo data. We show that with as little as
19 in vivo images, fine-tuning the pre-trained network improves the dice score
by 21% compared to training from scratch. We also demonstrate that if the same
number of natural and simulation US images is available, pre-training on
simulation data is preferable.
