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:骨髓癌是最有害的妇科疾病之一。 通过计算机辅助技术在早期检测卵巢肿瘤可以有效降低死亡率。 随着医疗标准的改进, 临床治疗中广泛应用超声波图像。 然而, 最近显著的方法主要侧重于单模式超声卵巢肿瘤分解或识别, 这意味着仍然缺乏研究探索多模式超声波通用肿瘤图象的表达能力。 为了解决这个问题, 我们建议使用多式软体骨骼超声波( MMOTU) 图像数据集, 包含1469 2 d 超声波图像和170 对比强化超声学( CEUUS) 图像, 具有像素和全局性图象。 根据 MMOTU, 我们主要侧重于未受监督的跨多声波骨髓细胞分解任务。 为了解决域变问题, 我们建议基于双向型双向目标直流的OVDEUMUMUTU(我们第一次设计源- 双向SDF) 和双向SMDM(S- deal IM) 和SIM 数据源的SIM- deal- demodial 和SDDDMDO 数据流数据转换。