## 5 Discussion
It should also be emphasised that the ultrasound is a rather demanding modality, which is reflected in the subjective interpretation of the imaging material. Particularly pressing is the accurate determination of object boundaries (e.g., for ovaries and follicles), which are often inexpressive and jagged. The complexity of interpreting ultrasound data from the USOVA3D database is, thus, reflected in the higher inter-observer variability (e.g., DSC and JCI coefficients much lower than 1). In our study, we chose S-Net [11], which is also based on the U-Net architecture, as a state-of-the-art for a comparison with our methods. [PERSON] et al. have shown in [11] that S-Net is currently the most effective follicle and ovary detection method. The latter was substantiated by 0.93 mean Sensitivity and by 0.92 (ovary) and 0.87 (follicle) mean Dice Similarity Coefficient (DSC) by detection, respectively. All metrics were calculated on 20 testing ovarian ultrasound volumes from their private database. To the best of our knowledge, their testing data and the code of S-Net are not publicly available, therefore, we implemented this method by ourselves, and tested it on the public USOVA3D database. The obtained results were evaluated both with our evaluation protocol and with the usual metrics (i.e., DSC, Jaccard index). It pointed out that our proposed 'EXT 1 + DS' and 'EXT 2 + DS' methods outperformed S-Net in virtually all metrics, both in follicle and ovary detection (see Tables 2, 3, 6 and 7). Besides, we note that the ranking of detection methods based on our evaluation protocol or based on established metrics is consistent, whereat the advantage of our protocol being that we obtain a single effectiveness estimate for each method and, therefore, the methods do not need to be re-ranked according to each of the individual metrics. The following should be also emphasised when comparing effectiveness of our methods and S-Net. S-Net achieved very high mean sensitivity (0.93) and mean DSC (around 0.9) by detection on private testing data. For the public USOVA3D database, however, we observe that even inter-rater variability with 0.88 Sensitivity and Dice Similarity Coefficient of 0.88 (ovary) or 0.86 ( follicles) is far behind the S-Net results. The latter indicates undoubtedly that USOVA3D is an extremely challenging database. In our opinion, a direct comparison of the calculated effectiveness metrics for our methods with the effectiveness metrics of similar works is not relevant, as different research groups have evaluated their solutions (some were designed for 2D ultrasound data) on their private ovarian ultrasound data. The problem of the large variation in the algorithm's effectiveness metrics, calculated on different data, was, in this study, demonstrated above in the case of the state-of-the-art S-Net method. [PERSON] et al. [9] compared in detail their 3D DWT detection method with selected advanced algorithms (including the SonoAVC algorithm integrated into General Electric ultrasound devices) on the same, i.e., today publicly available ovarian ultrasound data (at that time the USOVA3D database was not yet published). They demonstrated the superiority of their method by all criteria. This 3D DWT method was then added to the USOVA3D database as a baseline function 1. In this study, however, we proved experimentally that our proposed solutions surpassed the 3D DWT method in respect to the effectiveness. Based on all the results and analyses, our methods can also be considered the state-of-the-art in the field of Ovary and follicle detection. Our study is not a clinical study, so information about clinically acceptable detection errors was not available. The comparison was, therefore, made with an inter-observer variability. The DSC coefficient was lower by less than 2% and the JCI index was lower by less than 3% in respect to the inter-observer variability (i.e., an estimate of detection accuracy that we can expect from experts) when detecting ovaries with our best 'EXT 1 + DS' method.