In fish, female fecundity, generally defined as the number of eggs spawned, depends on the tightly regulated recruitment, growth and maturation of follicle-enclosed oocytes ranging from 20 μm to over 1000 μm in diameter. To better understand the regulatory mechanisms of fish fecundity, including the role of important novel players such as microRNAs, we still lack a comprehensive spatiotemporal model of follicular dynamics. Thus, our work aimed at developing a novel method to describe the follicular content and its organization in the entire ovary at different life stages of the medaka fish, a daily spawner with an asynchronous ovarian development. Using a confocal microscopy approach, we developed a complete 3D imaging and analysis workflow that overcomes the methodological biases of classical stereological 2D approaches. First, we established an efficient permeabilization and clearing procedures that allows staining and imaging of the entire ovary at both adult and larval stages. To achieve reliable 3D quantitative image analysis, we took advantage of the recent deep-learning algorithm Cellpose for cell segmentation (Stringer et al, 2021), in combination with other image processing tools for enhancement, denoising, filtering and reconstruction. Additionally, we applied this method to decipher the role of miR-202 during oogenesis, a key regulator of fish fecundity (Gay et al, 2018). This pipeline now allows a comprehensive and accurate assessment of ovarian content at different developmental stages, thus providing original data to help understanding the follicular growth dynamics and regulations at the wholeorgan level.