Background and objective: Automated follicle detection in ovarian ultrasound volumes remains a challenging task. An objective comparison of different follicle-detection approaches is only possible when all are tested on the same data. This paper describes the development and structure of the first publicly accessible USOVA3D database of annotated ultrasound volumes with ovarian follicles. Methods: The ovary and all follicles were annotated in each volume by two medical experts. The USOVA3D database is supplemented by a general verification protocol for unbiased assessment of detection algorithms that can be compared and ranked by scoring according to this protocol. This paper also introduces two baseline automated follicle-detection algorithms, the first based on Directional 3D Wavelet Transform (3D DWT) and the second based on Convolutional Neural Networks (CNN). Results: The USOVA3D testing data set was used to verify the variability and reliability of follicle annotations. The intra-rater overall score yielded around 83 (out of a maximum of 100), while both baseline algorithms pointed out just a slightly lower performance, with the 3D DWT-based algorithm being better, with an overall score around 78. Conclusions: On the other hand, the development of the CNN-based algorithm demonstrated that the USOVA3D database contains sufficient data for successful training without overfitting. The inter-rater reliability analysis and the obtained statistical metrics of effectiveness for both baseline algorithms confirmed that the USOVA3D database is a reliable source for developing new automated detection methods.

背景与目的：在卵巢超声检查中自动检测卵泡仍然是一项艰巨的任务。只有在对所有数据进行相同数据测试的情况下，才可能对不同的卵泡检测方法进行客观比较。本文介绍了第一个可公开访问的带有卵泡的带注释超声体积的USOVA3D数据库的开发和结构。方法：由两位医学专家对每卷中的卵巢和所有卵泡进行注释。USOVA3D数据库通过通用验证协议进行了补充，可以对检测算法进行无偏评估，可以根据此协议通过评分对它们进行比较和排名。本文还介绍了两种基线自动卵泡检测算法，第一种基于方向性3D小波变换（3D DWT），第二种基于卷积神经网络（CNN）。结果：USOVA3D测试数据集用于验证卵泡注释的可变性和可靠性。帧内评价者整体得分得到约83（下最大值为100），而基线算法指出只是稍微较低的性能，与基于DWT-3D算法是较好的，共有78的全部得分结论：在另一方面，基于CNN的算法的开发证明USOVA3D数据库包含足够的数据，可以成功进行训练而不会过度拟合。评估者间的可靠性分析和所获得的两种基线算法有效性的统计指标证实，USOVA3D数据库是开发新的自动检测方法的可靠来源。