Deep learning-based classification of the mouse estrous cycle stages
Kyohei Sano, Shingo Matsuda, Suguru Tohyama, Daisuke Komura, Eiji Shimizu, Chihiro Sutoh
Abstract
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.