## 1 Introduction
The second baseline algorithm, 2D UNET, demonstrates modern algorithm designing based on Deep Learning, the Convolutional Neural Networks (CNN) theory, and established 2D U-Net architecture. Both algorithms were evaluated on the USOVA3D testing set, and the baseline results (i.e., scores) were established for the follicle and ovary detection efficiency [7]. We also confirmed by thorough analysis that the USOVA3D database can be a reliable source for developing new detection methods. We deal with the development of effective learning-based object detection approaches in 3D medical imaging data using 3D Convolutional Neural Networks in this research. Although 2D CNN-based detection methods that process volumes in a slice-by-slice manner and then combine partial results into a whole, generally tend to be superior in respect of the 'true' 3D CNN-based detection methods that process volumes as a whole [13] (the reasons are often the need for an extremely large number of training samples and the huge computational complexity of 3D CNNs), the opposite will be demonstrated in this work. On the case of follicle and ovary detection in ovarian ultrasound volumes, we will confirm experimentally that it is possible to develop sophisticated 3D CNN-based methods that surpass 2D CNN-based methods and 3D methods based on 'hand-crafted' features. The development and verification of 3D detection procedures will be conducted by using the USOVA3D public database, which is basically a relatively small database. It is usually the lack of data that is the main reason for the lower efficiency of 3D CNN-based detection methods [13]. However, it will be proven in this research that we can develop an effective 3D detection method through appropriately supervised learning, despite the relatively small training set. This paper thus introduces advanced solutions based on three-dimensional CNNs for the follicle and ovary detection in ovarian ultrasound volumes. Our solutions are based on the established U-Net architecture [14; 15]. We have developed advanced methods for 3D object detection in volumes by using the Deep Supervision technique [16], and by replication of either the entire U-Net architecture or certain parts of the U-Net decoder. The proposed approaches were designed primarily for 3D follicle and ovary detection in ultrasound volumes. The effectiveness of the proposed methods was verified by using the USOVA3D database. The contribution of this research work is summarised in:  
1. The development of sophisticated two-stage CNN-based algorithms for 3D object detection, whereat the algorithms are built on the U-Net architecture and Deep Supervision;
2. Introduction of the most effective 3D algorithms for follicle and ovary detection, obtained by appropriately controlled training;
3. Effectiveness assessment of CNN-based object detection approaches by statistical evaluation of multiple model training repetitions. This article is structured as follows. A short overview of the USOVA3D public database and employed evaluation protocol is given in Section 2. Novel 3D object detection algorithms based on the U-Net architecture are described in detail in Section 3. In addition, guidelines are provided on how to adapt these methods to detect follicles and ovaries. Section 4 presents some of the results obtained on the USOVA3D database, followed by Section 5, which emphasises certain aspects of our detection methods. Section 6 concludes this paper briefly with some hints about future work.