To preserve the fertility of young female cancer patients, ovarian tissue cryopreservation and transplantation have been investigated as next-generation reproductive medical technologies. Non-invasive visualization of follicles in ovarian tissue and cryopreservation of higher density tissue is essential for effective transplantation. We proposed the use of optical coherence tomography (OCT) that can noninvasively visualize the internal structure of the ovarian tissue. However, a method for quantifying cell density has not yet been established because of the lack of available techniques to visualize follicles noninvasively. We proposed the use of a convolutional neural network (CNN) to extract small features from medical images as an image analysis method to automatically detect follicles from the obtained OCT images. First, we collected a total of 13 ovarian tissues from four-day-old mice and acquired OCT images using a full-field-type OCT. Then, the acquired images were analyzed using three detection methods: filter processing, filter processing combined with the CNN, and only CNN. Finally, to verify the detection accuracy of each method, the detection rate and precision were calculated by taking the doctor’s detection as the correct result. The results showed that the detection method only using CNN achieved a detection rate of 0.81 and precision of 0.67; this indicated that follicles could be effectively detected using our proposed method. Furthermore, it is quantitatively evident that the density of follicles from the surface layer to the deep region differs depending on the tissue. In the future, these results could be used to detect follicles in tissues of different maturation stages and quantify follicles three-dimensionally, further accelerating next-generation reproductive medicine.

为了保持年轻女性癌症患者的生育能力，卵巢组织冷冻保存和移植已被作为下一代生殖医学技术进行研究。卵巢组织中卵泡的非侵入性可视化和高密度组织的冷冻保存对于有效移植至关重要。我们建议使用光学相干断层扫描 (OCT)，它可以无创地显示卵巢组织的内部结构。然而，由于缺乏无创可视化卵泡的可用技术，尚未建立量化细胞密度的方法。我们提出使用卷积神经网络 (CNN) 从医学图像中提取小特征作为一种图像分析方法，从获得的 OCT 图像中自动检测卵泡。第一的，我们从四天大的小鼠身上收集了总共 13 个卵巢组织，并使用全视野型 OCT 获取了 OCT 图像。然后，使用三种检测方法对获取的图像进行分析：滤波处理、滤波处理结合 CNN 和仅 CNN。最后，为了验证每种方法的检测精度，以医生的检测为正确结果，计算检测率和精度。结果表明，仅使用CNN的检测方法达到了0.81的检测率和0.67的精度；这表明使用我们提出的方法可以有效地检测到卵泡。此外，从表面层到深层区域的毛囊密度随组织的不同而有所不同，这在数量上是显而易见的。将来，