arXiv 2605.267742026-06-02cs.CV

经阴道超声图像中的剖宫产瘢痕缺损分割：数据集与基准

Cesarean Scar Defect Segmentation in Transvaginal Ultrasound Images: a Dataset and Benchmark

Department of Obstetrics and Gynecology, International Peace Maternity and Child Health Hospital affiliated to Shanghai Jiao Tong University School of Medicine（妇产科部门，上海交通大学医学院国际和平妇产儿童医院）；School of Computer Science, University of Nottingham Ningbo China（Nottingham Ningbo中国大学计算机学院）；School of Computer Science, University of Nottingham（Nottingham大学计算机学院）；Department of Computer Science and Engineering, University of California, San Diego（加州大学圣地亚哥分校计算机科学与工程系）；Department of Ultrasound, International Peace Maternity and Child Health Hospital affiliated to Shanghai Jiao Tong University School of Medicine（超声科，上海交通大学医学院国际和平妇产儿童医院）；School of Electronic Information and Electrical Engineering, Shanghai Jiao Tong University（上海交通大学电子信息与电气工程学院）；Department of Cardiology, Gold Coast University Hospital（心内科，Gold Coast大学医院）

Yuan Tian, Yue Li, Wei Xia, Tianyu Xu, Jian Zhang, Liye Shi, Jing Liu, Yang Wang, Ming Liu, Qing Xu, Yixuan Zhang, Maggie M. He, Xiangjian He

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AI总结：

针对经阴道超声图像中剖宫产瘢痕缺损（CSD）分割缺乏公开数据集的问题，构建了包含1111张图像和16个视频的CSD数据集，提供像素级标注，并建立了基准以推动医学图像分割算法和临床创新。

摘要：

剖宫产瘢痕缺损（CSD）是剖宫产后最常见的并发症之一。经阴道超声检查广泛用于CSD的初步筛查。准确确定CSD的轮廓和尺寸对治疗至关重要。然而，由于CSD尺寸小、形态不规则、图像质量欠佳以及资源有限环境中临床意识不足，超声医师常常忽略CSD。尽管人工智能在医学影像领域取得了进展，但目前尚无公开的经阴道超声CSD分割数据集。为填补这一空白，我们提出了一个全面的CSD数据集，包含1111张图像和16个视频，共501个阳性样本，带有确证的CSD和精确的像素级手动标注。标注遵循标准化临床指南，由经验丰富的超声医师和受过培训的博士生合作完成。这项工作为推进医学图像分割算法和促进临床创新提供了高质量的基准资源。最终，改善CSD诊断及后续治疗策略可提高育龄女性的生活质量，对医学研究和临床实践均具有重要价值。

英文摘要：

Cesarean Scar Defect (CSD) is one of the most prevalent complications following cesarean delivery. Transvaginal ultrasonography is widely used for primary CSD screening. Accurate determination of CSD outline and dimensions is crucial for treatment. However, CSDs are frequently overlooked by sonographers due to small size and irregular morphology, suboptimal image quality, and limited clinical awareness in resource-constrained settings. Despite artificial intelligence advances in medical imaging, no public dataset exists for transvaginal ultrasound CSD segmentation. To address this gap, we present a comprehensive CSD dataset comprising 1,111 images and 16 videos, yielding 501 positive samples with confirmed CSD and precise pixel-level manual annotations. Annotations are performed following standardized clinical guidelines through collaboration between experienced sonographers and trained PhD students. This work provides high-quality benchmark resources for advancing medical image segmentation algorithms and promoting clinical innovation. Ultimately, improved CSD diagnosis and subsequent treatment strategies can enhance the quality of life in women of reproductive age, representing significant value for both medical research and clinical practice.

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