mlsanityData to 7 Oct 2026

MMOTU: A Multi-Modality Ovarian Tumor Ultrasound Image Dataset for Unsupervised Cross-Domain Semantic Segmentation

arXiv 2207.06799 ↗First posted 14 Jul 2022cs.CVcited by 14

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Abstract (arXiv)

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.

Key facts. Cited by 14 papers in the map (data through 7 Oct 2026). Builds on 30 of its 61 references that are in the map. First posted on arXiv 14 Jul 2022.

Builds on

30 of 61 references.

References · 30

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18 more
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Cited by year (2026 to 7 Oct)

Citing papers per year: 2024: 2; 2025: 4; 2026: 8.

14 papers.

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  1. 2024Segment anything model 2: an application to 2D and 3D medical imagescited by 27
  2. 2025USF-MAE: Ultrasound Self-Supervised Foundation Model with Masked Autoencodingcited by 16
  3. 2025Revisiting Data Augmentation for Ultrasound Imagescited by 6
  4. 2025MedicalNarratives: Connecting Medical Vision and Language with Localized Narrativescited by 2
  5. 2026A texture-based framework for foundational ultrasound modelscited by 1
  6. 2026UltraSAM3: A Concept-Driven Foundation Model for Universal Ultrasound Image Segmentationcited by 1
  7. 2024Cross-Organ Domain Adaptive Neural Network for Pancreatic Endoscopic Ultrasound Image Segmentationcited by 0
  8. 2025ViTaL: A Multimodality Dataset and Benchmark for Multi-pathological Ovarian Tumor Recognitioncited by 0
  9. 2026US-JEPA: A Joint Embedding Predictive Architecture for Ultrasoundcited by 0
  10. 2026Feature-Space Guided Diffusion for Realistic Ultrasound Image Synthesiscited by 0
  11. 2026Cross-Modal MRI Ovary Segmentation in Endometriosis Using Unpaired TVUS Prototype Priorscited by 0
  12. 2026Less Contouring, More Accuracy: Lesion-Guided ROI Deep Learning for Ovarian Ultrasound Classificationcited by 0
2 more
  1. 2026UltraBench 2: Towards Robust Evaluation of Vision Foundation Models on Ultrasoundcited by 0
  2. 2026UltraG-Bench: A Multi-task Benchmark for assessing Large Vision-Language Models on Pixel-level Evidence Grounding in Ultrasoundcited by 0