## 5 Discussion
In the detection of follicles, however, Sensitivity was lower by about 8%, the DSC coefficient by about 6% and the JCI index by about 7% in respect to the inter-observer variability. In summary, our best methods are, for ovary detection, in the range of the experts' accuracy, while for follicle detection, we are still behind the experts, and, therefore, it would be necessary to verify the results manually in the clinical practice. Figure 8 depicts some typical qualitative results for the better compared methods. Computer detected follicles and ovary are superimposed on the selected cross-sections of ovarian ultrasound volume. The difficulty of detection in the USOVA3D database can be seen clearly, as the edges of the follicles and ovary are very indistinct. Annotations of rare 1 for this volume and these cross-sections are shown in Figure 1.  
Let us also consider the capacity of our CNN models. The original 3D U-Net has a little over 4.82 Million (M) of free parameters, while this number increased by 484 when the Deep Supervision was integrated. Both 3D U-Net extensions were designed primarily to detect the ovaries, however, they were also applied successfully for follicle detection. The EXT 1 architecture has a total of 9.64 M parameters, of which 4.82 M parameters are frozen or fixed by Transfer Learning (if the case of ovary detection), respectively. The frozen parameters are for the first stage of the EXT 1 model. The proposed EXT 2 extension is more complex, as it has a total of 11.41 M parameters, whereas also 4.82 M parameters from the first stage are frozen or pretrained (by ovary detection), respectively. The integration of Deep Supervision in the 3D U-Net extensions contributed an additional 968 parameters, of which 484 were trainable (by ovary detection). In contrast, although 2D UNET has a total of 31.13 M parameters, which is almost 3 times more than our proposed methods, its effectiveness of follicle and ovary detection is remarkably inferior to our methods. Our deep models were indeed trained on a small number of volumes (16) as there are no more training data available in the USOVA3D database. The lack of data was mitigated by meaningful preprocessing and the use of augmentation. The fact that we train our CNN models of segmentation, where each output voxel represents its own training sample, also contributed to the successful training of our models. The number of voxels in our volumes is, of course, extremely large. (It is also true that samples are not completely independent.) We did not diagnose the overfitting problem when training our CNNs by USOVA3D data. CNN training is computationally demanding. An exhaustive experimentation with 50 repetitions of CNN models' training was performed by using the HPC RIVR MAISTER  
Figure 8: Qualitative results for the better compared methods. Detected follicles and ovary are superimposed on the selected cross-sections: (**a**) ‘EXT 1 + DS (best)’ method; (**b**) ‘EXT 2 + DS (last)’ method; (**c**) ‘SNET (best)’ method; (**d**) ‘3D UNET + DS (best)’ method. [PERSON]’s annotations are shown in Figure 1.  
powerful public supercomputer in Maribor, Slovenia ([[https://www.hpc-rivr.si](https://www.hpc-rivr.si)]([https://www.hpc-rivr.si](https://www.hpc-rivr.si)), accessed on 1 January 2022). Six dual-processor compute nodes, each with 4 additional Nvidia Tesla V100 Graphics Processing Units, GPU (each GPU had 32 GB of RAM), with a total of 122,952 cores, were utilised on this supercomputer. Follicle detection training took about 3 s per step, or about 70 s per epoch. On the other hand, the EXT 1 and EXT 2 extensions, developed primarily for the ovary detection, took up to 5 s for the training step and about 120 s per epoch, respectively. The trained network conducted an inference in around 9 s per volume, which also includes all volume resizing, and storing the result on the secondary memory.