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.

翻译：卵巢癌是最为有害的妇科疾病之一。通过计算机辅助技术早期检测卵巢肿瘤可有效降低死亡率。随着医疗标准的提升，超声图像在临床诊疗中得到广泛应用。然而，当前主流方法主要聚焦于单模态超声图像中的卵巢肿瘤分割或识别任务，这意味着针对多模态超声卵巢肿瘤图像表征能力的研究仍显不足。为解决该问题，我们提出一个包含1469张二维超声图像和170张对比增强超声图像（CEUS）的多模态卵巢肿瘤超声（MMOTU）图像数据集，所有图像均提供像素级和全局级标注。基于MMOTU数据集，我们重点研究无监督跨域语义分割任务。为应对域偏移问题，我们提出名为双方案域选择网络（DS2Net）的基于特征对齐的架构。具体而言，首先设计源域编码器和目标域编码器分别提取源域与目标图像的双风格特征；其次提出域特异选择模块（DDSM）和域通用选择模块（DUSM），用于提取两种风格（源风格或目标风格）中的特异特征与通用特征；最后融合这两类特征并将其输入源域解码器和目标解码器以生成最终预测结果。在MMOTU图像数据集上的大量对比实验与分析表明，DS2Net能够提升二维超声图像与CEUS图像双向跨域适应的分割性能。本研究所提出的数据集与代码均可在https://github.com/cv516Buaa/MMOTU_DS2Net获取。