## 6 Conclusions  
The main intention of this paper was to introduce efficient 3D object detection algorithms, aimed primarily to detect follicles and ovaries in ultrasound volumes. We took a learning-based design approach, relying on the established U-Net architecture, and upgrading it in this research. Two methods for indirect or two-stage object detection were developed respectively, namely, in the first solution the entire U-Net architecture was duplicated, while, in the second solution, just certain parts of the U-Net model decoder were replicated. The first stage of such CNN introduces a kind of prior knowledge into the detection process, as it directs the'second stage' to that part of the 3D space (volume) where the searched object is more likely to be located. Deep Supervision was integrated into both CNNs as well, which had a positive effect on the training of the lower layers of the Neural Network. The proposed methods were verified by the detection of follicles and ovaries in ultrasound volumes. The methods were trained end-to-end by follicle detection, while an idea of Transfer Learning was utilised by ovary detection. The latter means that the 'first stage' of the CNN was trained separately on the problem of follicle detection, and, afterwards, the trained 'first stage' was, by Transfer Learning, employed by training the'second stage' to detect ovaries. The follicle detection results pointed out that our proposed U-Net extensions did not statistically significantly improve the results of the with Deep Supervision integrated 3D U-Net. However, we obtained higher effectiveness than 3D U-Net (+ Deep Supervision) by some repetitions of our CNNs' training. On the other hand, the superiority of our proposed methods was indisputable in the detection of ovaries. The results pointed out up to 7.6% more accurate detection compared to the up-to-date automated ovary detection methods. Our two-stage CNNs estimated follicles only slightly worse than the raters, while our methods estimated the ovaries with almost the same accuracy as the raters. We verified by quantitative metrics that our proposed methods, both in the case of follicle and ovary detection, are more effective than the USOVA3D baseline functions and the state-of-the-art method S-Net [11] on the very challenging USOVA3D testing data. We demonstrated that the improvements are not only due to the random initialisation of the CNN models, but that, by using the proposed modifications of the U-Net architecture, the follicles and ovaries were detected more accurately in a systematic way. By analysing 50 repetitions of training (and testing) of our CNNs statistically, we proved that the training is stable, and that in practically every other repetition the CNN is constructed, which is more efficient than the most accurate methods for detecting follicles and ovaries so far. In addition, it was substantiated that, despite using the small USOVA3D database, the detection algorithms can be trained quite successfully and without any data overfitting. A convergence was reached in a reasonable number of training steps. Let us conclude this paper with some future work directions. One of the succeeding researches will be focused on applying and transferring our solutions to other problem domains. The aim will be to demonstrate that only minimal interventions are needed in our proposed detection algorithms. In the field of Ovarian Ultrasound Volumes' Processing, we will upgrade our solutions further, in order to detect ovaries accurately in just one pass, without adding prior knowledge about follicles. Special attention will be paid to the augmentation of the small USOVA3D training set. Finally, we recommend that the statistical assessment of repetitive training and testing becomes the rule when also reporting results in the field of CNN-based approaches. Conceptualization, B.P. and M.S. ; methodology, B.P. and M.S. ; software, M.S. and B.P. ; validation, B.P. and M.S. ; formal analysis, B.P. ; investigation, B.P. ; resources, B.P. ; data curation, B.P.