Public database for validation of follicle detection algorithms on 3D ultrasound images of ovaries. (November 2020)

• Record Type: Journal Article
• Authors: Potočnik, Božidar; Munda, Jurij; Reljič, Milan; Rakić, Ksenija; Knez, Jure; Vlaisavljević, Veljko; Sedej, Gašper; Cigale, Boris; Holobar, Aleš; Zazula, Damjan
• Is Part Of: Computer methods and programs in biomedicine. Volume 196(2020)
• Publication Date: 2020-11
• Subjects: 3D Ultrasound images of ovaries -- Detection of ovarian follicles -- Public database -- Unbiased verification of detection algorithms -- Web services; Medicine -- Computer programs -- Periodicals; Biology -- Computer programs -- Periodicals; Computers -- Periodicals; Medicine -- Periodicals; Médecine -- Logiciels -- Périodiques; Biologie -- Logiciels -- Périodiques; Biology -- Computer programs; Medicine -- Computer programs; Periodicals; Electronic journals; 610.28
• DOI: 10.1016/j.cmpb.2020.105621
• Languages: English
• ISSNs: 0169-2607

Abstract

Highlights: Publishing of the USOVA3D public database of annotated 3D ovarian ultrasound images. Ovaries and follicles annotated by two gynaecologists. Design of a verification protocol for unbiased assessment of detection algorithms. Introduction of two advanced algorithms for follicle and ovary detection. Inter-rater variability and baseline performance assessed on this database. Abstract: 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.