In multiple spawning fish, oogenesis involves anatomical structures in permanent turnover, the ovarian follicles, which support the development and growth of oocytes until spawning. Although many regulatory actors have already been identified, we still have an incomplete view of the dynamics of the follicular growth during fish life. Traditionally, manual methods of counting dissociated follicles or semi-automatic methods from two-dimensional (2D) ovarian sections have been used to unravel ovarian content, but these are time-consuming and severely limit data accuracy. Here, we exploited novel 3D approaches to precisely enumerate the follicles population. Collected ovaries were subjected to clearing (combining CUBIC and ECi protocols) to allow confocal 3D imaging. We then exploited recent Deep Learning algorithms (Noise2Void, Cellpose) that were integrated into a processing pipeline for reliable measurement of the follicle diameters. The resulting follicular diameter distributions reveal different patterns of follicular density over the fish life, starting with a synchronous phase at the larva sages that converges to an asynchronous stationary state in the adult. Furthermore, in females mutant for miR-202 (KO mir-202-/-), a known regulator of fish fecundity, follicular density analyses show a clear disruption of the oocyte reserve management, which is most likely to be at the origin of the decreased fecundity. Such quantitative and temporal analysis of the ovary in 3D thus allowed to describe precisely the dynamics of the follicular reserve and more generally of the asynchronous oogenesis. This approach also allowed to highlight the role of mir-202 in the regulation of this reserve.