Deep learning-based classification of the mouse estrous cycle stages
Deep learning-based classification of the mouse estrous cycle stages
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DOI：
10.1038/s41598-020-68611-0
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发表时间：
2020-07-16
影响因子：
4.6
通讯作者：
Sutoh, Chihiro
中科院分区：
文献类型：
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作者：
Sano, Kyohei;Matsuda, Shingo;Sutoh, Chihiro
临床前动物对雌性动物的需求迅速增长，因此有必要通过阴道涂片细胞学来确定动物的动情周期阶段。但发情阶段的确定需要大量的训练，耗时长，成本高；此外，人类检查员获得的结果可能并不一致。在这里，我们报告了一个用 2,096 个显微图像训练的机器学习模型，我们将其命名为“使用图像识别技术 (SECREIT) 的 RodEnt 发情周期阶段估计器”。通过测试数据集（736 张图像），SECREIT 在每个动情阶段的接收者操作特征曲线下面积达到了 0.962 或更高。使用 100 张图像进行的测试表明，SECREIT 在 11 秒内提供了正确的分类，与两名人类检查员提供的分类相似（SECREIT：91％，Human 1：91％，Human 2：79％）。这个秘密可能是加速雌性啮齿动物研究的第一步。
There is a rapidly growing demand for female animals in preclinical animal, and thus it is necessary to determine animals' estrous cycle stages from vaginal smear cytology. However, the determination of estrous stages requires extensive training, takes a long time, and is costly; moreover, the results obtained by human examiners may not be consistent. Here, we report a machine learning model trained with 2,096 microscopic images that we named the "Stage Estimator of estrous Cycle of RodEnt using an Image-recognition Technique (SECREIT)." With the test dataset (736 images), SECREIT achieved area under the receiver-operating-characteristic curve of 0.962 or more for each estrous stage. A test using 100 images showed that SECREIT provided correct classification that was similar to that provided by two human examiners (SECREIT: 91%, Human 1: 91%, Human 2: 79%) in 11 s. The SECREIT can be a first step toward accelerating the research using female rodents.