发表机构
Hunan University; The Hong Kong University of Science and Technology; Anhui University; Shenzhen Maternity and Child Healthcare Hospital; Sichuan Provincial Maternity and Child Health Care Hospital; National University of Singapore; Yunnan Maternal and Child Health Hospital; Zhejiang University（湖南大学; 香港科技大学; 安徽大学; 深圳市妇幼保健院; 四川省妇幼保健院; 新加坡国立大学; 云南省妇幼保健院; 浙江大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
本研究提出首个公开的早孕胎儿超声筛查多中心基准FUSEP数据集，含4017张超声图像及45820个标注，评估了多种学习方法在相关任务的性能，助力医学智能辅助诊断等任务开发。
AI 中文摘要
全球每年有大量先天性异常的婴儿出生，尤其是在医疗资源欠发达地区。目前，胎儿超声筛查是早孕解剖检测最常用的模态，该模态可更早检测出异常并提供合适的治疗建议。然而，早孕阶段超声数据集的缺乏延缓了自动辅助诊断的发展。本研究中，我们提出了名为FUSEP的早孕胎儿超声筛查基准数据集，以促进智能超声检查和辅助诊断。该数据集包含国际指南推荐的两个超声视图，即来自三家医院的头臀长（CRL）视图和颈项透明层（NT）视图，总计4017张超声图像，带有45820个专家级框级标注。我们的数据集和基准有三项贡献：1）医学专家在两个视图中用框级格式标注了共14个关键解剖结构；2）数据广泛收集自不同超声医师、设备、扫描角度、医院等；3）报告了半监督学习、全监督学习、无监督域适应（UDA）和无来源UDA在超声图像多目标检测中的性能。据我们所知，这是首个公开的早孕胎儿超声筛查数据集和基准。我们认为FUSEP及基准可助力医学界开发标准平面识别、超声图像质量控制、早孕自动辅助诊断、医学多目标检测、目标检测域适应等多项任务。
英文摘要
A large number of infants with congenital anomalies are born each year globally, especially in areas with underdeveloped medical resources. Currently, fetal ultrasound screening is the most common modality for early pregnancy anatomy detection. This modality can detect anomalies earlier and provide opportune treatment advice. However, the lack of an ultrasound dataset on early fetal gestation has slowed down the development of automated assisted diagnosis. In this work, we present a benchmark dataset for Fetal Ultrasound Screening in Early Pregnancy to facilitate intelligent ultrasound examination and assisted diagnosis called FUSEP. Our dataset consists of two ultrasound views recommended by the international guideline, i.e., Crown-rump Length (CRL) and Nuchal Translucency (NT) views in three hospitals, totaling 4,017 ultrasound images, with 45,820 box-level expert-level annotations. Our dataset and baseline present the following three contributions: 1) Our medical experts annotated a total of 14 key anatomical structures in two views using a box-level format; 2) Our data is collected extensively from different sonographers, devices, scanning angles, hospitals, etc; 3) We report the performance of the semi-supervised learning, fully supervised learning, unsupervised domain adaptation (UDA), and source-free UDA in ultrasound images multi-object detection. To the best of our knowledge, this is the first publicly available dataset and benchmark for fetal early pregnancy ultrasound screening. We believe that FUSEP and benchmark can contribute to the medical community in the development of multiple tasks such as standard plane recognition, quality control on ultrasound images, automated assisted diagnostics in early fetal pregnancy, medical multi-object detection, domain adaptation for object detection, etc.
