## 1 Introduction
The efficiency (usually sensitivity and precision were used as the metrics) and accuracy (metrics mean absolute distance between the detected and  
Figure 1: Sample ultrasound volume from the USOVA3D database: (**a**) 2D views of an annotated ovary (ochre) with follicles (coloured) superimposed on the selected cross-sections: ZY plane (left), ZX plane (middle) and XY plane (right); (**b**) A 3D view of the cross-sections, shown in (**a**), through volume with annotated follicles; and (**c**) The 3D view of the corresponding ovary. annotated follicle) of detection approaches have increased with this development. 3D ultrasound devices began to appear massively around 2000, and in a few years became the de facto Standard in the field of Obstetrics and Gynaecology. Simultaneously, the development of follicle detection approaches has shifted from methods designed to process 2D cross-sections through the ovary to true 3D detection methods that process 3D ovarian ultrasound volumes as a whole. Among them we find successful methods based either on continuous wavelets, levels sets, or on trained probabilistic frameworks of ovary and follicle models [5; 7]. The proprietary semi-automated detection algorithm SonoAVC [8], incorporated in the General Electric ultrasound devices designed for automated volume calculation, should also be pointed out. The 3D follicle detection method based on the Directional 3D Wavelet Transform (3D DWT) [9], developed by our research group, has proven to be also very efficient and accurate. Recently, Deep Learning based approaches have been proposed for follicle and ovary detection. The CR-Unet network [10] upgraded a 2D U-Net architecture by spatial Recurrent Neural Network (RNN) modules. These RNN modules were used to learn large scale spatial features in the segmentation model. The model was trained to detect ovaries and follicles simultaneously as a three class segmentation problem. This model was, indeed, trained on 2D ultrasound slices, but can also be applied in a slice-by-slice manner to 3D volumes. The S-Net network [11] also utilised the 2D U-Net, with the difference that several slices of a 3D volume were processed at once. Such processing enabled the extraction of additional 3D information, and, thus, reduced discontinuities in the 3D segments. S-Net treated ovary and follicle detection as two binary segmentation problems. It used a special composite Binary Cross Entropy loss function that gave an additional penalty to follicle detections outside the ovary. Both mentioned Deep Neural Networks were trained and evaluated on proprietary databases, which makes direct comparisons with these results difficult. However, the methods in [10; 11] were compared directly, demonstrating S-Net [11] to be superior. After a brief review, we concluded that this research field is a mature, but still active field, as the development of improved computer follicle detection approaches still challenges many researchers [5; 9; 10; 11; 12]. Already in our review article [5] we identified the problem of unbiased comparison of follicle detection algorithms. In order to validate the solutions, different research groups, namely, use all sorts of metrics, evaluated on their own image datasets, in which the follicles (ovaries) are annotated manually by experts according to their own protocol. Such indefiniteness limits the objective comparison of different follicle-detection approaches. In our previous work [7], we therefore published the USOVA3D public database of annotated 3D ultrasound images of ovaries, which was supplemented with a precisely specified verification protocol for unbiased assessment of general detection algorithms. Additionally, two baseline algorithms were introduced for follicle and ovary detection. The first algorithm, 3D DWT, uses heuristic features and a classical approach to designing algorithms. In fact, it is a small upgrade of our most efficient follicle detection method to date [9].