Department of Computer Engineering, Gaziosmanpaşa University, Tokat, Turkey.
Department of Histology and Embryology, Erciyes University School of Medicine, Kayseri, Turkey.
Comput Biol Med. 2019 Sep;112:103350. doi: 10.1016/j.compbiomed.2019.103350. Epub 2019 Jul 9.
The ovary is a complex endocrine organ that shows significant structural and functional changes in the female reproductive system over recurrent cycles. There are different types of follicles in the ovarian tissue. The reproductive potential of each individual depends on the numbers of these follicles. However, genetic mutations, toxins, and some specific drugs have an effect on follicles. To determine these effects, it is of great importance to count the follicles. The number of follicles in the ovary is usually counted manually by experts, which is a tedious, time-consuming and intense process. In some cases, the experts count the follicles in a subjective way due to their knowledge. In this study, for the first time, a method has been proposed for automatically counting the follicles of ovarian tissue. Our method primarily involves filter-based segmentation applied to whole slide histological images, based on a convolutional neural network (CNN). A new method is also proposed to eliminate the noise that occurs after the segmentation process and to determine the boundaries of the follicles. Finally, the follicles whose boundaries are determined are classified. To evaluate its performance, the results of the proposed method were compared with those obtained by two different experts and the results of the Faster R-CNN model. The number of follicles obtained by the proposed method was very close to the number of follicles counted by the experts. It was also found that the proposed method was much more successful than the Faster R-CNN model.
卵巢是一个复杂的内分泌器官，在女性生殖系统中经历周期性的结构和功能变化。卵巢组织中有不同类型的卵泡。个体的生殖潜能取决于这些卵泡的数量。然而，遗传突变、毒素和一些特定的药物会对卵泡产生影响。为了确定这些影响，计数卵泡非常重要。卵巢中的卵泡数量通常由专家手动计数，这是一个繁琐、耗时且费力的过程。在某些情况下，由于专家的知识，他们会以主观的方式计数卵泡。在这项研究中，首次提出了一种自动计数卵巢组织卵泡的方法。我们的方法主要涉及基于卷积神经网络（CNN）的基于滤波器的分割应用于全幻灯片组织学图像。还提出了一种新的方法来消除分割过程后出现的噪声，并确定卵泡的边界。最后，对确定边界的卵泡进行分类。为了评估其性能，将所提出方法的结果与两位不同专家和 Faster R-CNN 模型的结果进行了比较。所提出方法获得的卵泡数量与专家计数的卵泡数量非常接近。还发现，所提出的方法比 Faster R-CNN 模型成功得多。
