  Deep neural networks (DNNs) have been widely used for medical image analysis.
However, the lack of access a to large-scale annotated dataset poses a great
challenge, especially in the case of rare diseases, or new domains for the
research society. Transfer of pre-trained features, from the relatively large
dataset is a considerable solution. In this paper, we have explored supervised
segmentation using domain adaptation for optic nerve and orbital tumor, when
only small sampled CT images are given. Even the lung image database consortium
image collection (LIDC-IDRI) is a cross-domain to orbital CT, but the proposed
domain adaptation method improved the performance of attention U-Net for the
segmentation in public optic nerve dataset and our clinical orbital tumor
dataset. The code and dataset are available at https://github.com/cmcbigdata.
