## 5 Discussion
The average overall score increased at best by 6.1 (EXT 1) and 7.5 (EXT 2) respectively, simultaneously increasing training stability (see Mean and Standard Deviation in Table 5). A similar trend was observed for the median of the overall algorithm score. It should be noted that the mere integration of Deep Supervision into the original 3D U-Net has practically not improved the effectiveness of ovary detection. An additional comment is needed when assessing the results using the best models on the validation set (denoted 'best'), or the models after the last training step ('last'), respectively. One would expect that, if a model is effective on a validation set, it will also be effective on a testing set, and vice versa. However, a certain inconsistency was noticed in Section 4. The reason is sought in the small USOVA3D database for which the testing set was determined manually without serious analysis [7]. The training set (with the validation set as part of it) does not summarise/reflect the statistics of the data in the testing set credibly, and, as a consequence, there were oscillations in performance by the 'best' and 'last' models. Let us analyse the effectiveness of follicle and ovary detection using our proposed methods in this sequel. For follicle detection, it pointed out that the effectiveness of the 3D DWT baseline function was surpassed with the best runs of the '3D UNET + DS' method and our proposed methods (see Table 2). As mentioned earlier, it can be misleading to observe only the results of the best training run. From Table 1 it can be noticed that the median of the '3D UNET + DS (last)' and 'EXT 2 + DS (last)' methods are almost equal to the result of the 3D DWT baseline function. Based on the median and mean values, we concluded that a result better or equal to the USOVA3D baseline result would be got from every other run of these two methods. The latter undoubtedly confirms that our proposed method also improved the successfulness of the 3D DWT baseline function, and that the higher overall algorithm score was not merely due to the different initialisations of the CNN models. A similar conclusion can be drawn for our 3D ovary detection methods. The obtained results with the best runs of our approaches were at least in the rank of the better 2D UNET baseline function, or the baseline results were exceeded in most cases, respectively. When using both U-Net extensions with integrated Deep Supervision, the results of the best runs were alongside the inter-rater variability, while, in the case of the 'EXT 1 + DS' method, this variability was even exceeded. The mean and median statistics in Table 5 are more conclusive, which first reveal that, so far, the best 2D UNET (baseline) method was surpassed by both the 'EXT 1 + DS' and 'EXT 2 + DS' methods, and, at the same time, our proposed methods are still behind the accuracy of the raters. We notice in Table 6 that the inter-rater variability for the ovaries is importantly higher than for the follicles (i.e., lower \(\xi_{alg}\) for ovaries than for follicles). This discrepancy in the raters' annotations (labels) certainly affects the CNN models' training with greater instability, thereby influencing the variation of the obtained results and their scatter (e.g., higher Standard Deviation). Let us emphasise once again that the labels of both raters were considered equally in the training. Adding noise to training data is one of regularisation techniques that reduces the possibility of CNN overfitting [23]. Based on the Dice Similarity Coefficient calculated between both raters, we ascertained that the raters annotated the same ovarian ultrasound volume to some extent differently. Nevertheless, we passed both non-identical annotations for the same structure (i.e., for ovary or follicle) into the training, which, of course, introduced some noise into the data, but at the same time prevented the CNN from overfitting.