  Ovarian cancer is one of the most harmful gynecological diseases. Detecting
ovarian tumors in early stage with computer-aided techniques can efficiently
decrease the mortality rate. With the improvement of medical treatment
standard, ultrasound images are widely applied in clinical treatment. However,
recent notable methods mainly focus on single-modality ultrasound ovarian tumor
segmentation or recognition, which means there still lacks researches on
exploring the representation capability of multi-modality ultrasound ovarian
tumor images. To solve this problem, we propose a Multi-Modality Ovarian Tumor
Ultrasound (MMOTU) image dataset containing 1469 2d ultrasound images and 170
contrast enhanced ultrasonography (CEUS) images with pixel-wise and global-wise
annotations. Based on MMOTU, we mainly focus on unsupervised cross-domain
semantic segmentation task. To solve the domain shift problem, we propose a
feature alignment based architecture named Dual-Scheme Domain-Selected Network
(DS2Net). Specifically, we first design source-encoder and target-encoder to
extract two-style features of source and target images. Then, we propose
Domain-Distinct Selected Module (DDSM) and Domain-Universal Selected Module
(DUSM) to extract the distinct and universal features in two styles
(source-style or target-style). Finally, we fuse these two kinds of features
and feed them into the source-decoder and target-decoder to generate final
predictions. Extensive comparison experiments and analysis on MMOTU image
dataset show that DS2Net can boost the segmentation performance for
bidirectional cross-domain adaptation of 2d ultrasound images and CEUS images.
Our proposed dataset and code are all available at
https://github.com/cv516Buaa/MMOTU_DS2Net.
