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- Title
WISE: whole-scenario embryo identification using self-supervised learning encoder in IVF.
- Authors
Liu, Mark; Lee, Chun-I; Tzeng, Chii-Ruey; Lai, Hsing-Hua; Huang, Yulun; Chang, T. Arthur
- Abstract
Purpose: To study the effectiveness of whole-scenario embryo identification using a self-supervised learning encoder (WISE) in in vitro fertilization (IVF) on time-lapse, cross-device, and cryo-thawed scenarios. Methods: WISE was based on the vision transformer (ViT) architecture and masked autoencoders (MAE), a self-supervised learning (SSL) method. To train WISE, we prepared three datasets including the SSL pre-training dataset, the time-lapse identification dataset, and the cross-device identification dataset. To identify whether pairs of images were from the same embryos in different scenarios in the downstream identification tasks, embryo images including time-lapse and microscope images were first pre-processed through object detection, cropping, padding, and resizing, and then fed into WISE to get predictions. Results: WISE could accurately identify embryos in the three scenarios. The accuracy was 99.89% on the time-lapse identification dataset, and 83.55% on the cross-device identification dataset. Besides, we subdivided a cryo-thawed evaluation set from the cross-device test set to have a better estimation of how WISE performs in the real-world, and it reached an accuracy of 82.22%. There were approximately 10% improvements in cross-device and cryo-thawed identification tasks after the SSL method was applied. Besides, WISE demonstrated improvements in the accuracy of 9.5%, 12%, and 18% over embryologists in the three scenarios. Conclusion: SSL methods can improve embryo identification accuracy even when dealing with cross-device and cryo-thawed paired images. The study is the first to apply SSL in embryo identification, and the results show the promise of WISE for future application in embryo witnessing.
- Subjects
TRANSFORMER models; EMBRYOS; FERTILIZATION in vitro; EMBRYO transfer; IDENTIFICATION
- Publication
Journal of Assisted Reproduction & Genetics, 2024, Vol 41, Issue 4, p967
- ISSN
1058-0468
- Publication type
Article
- DOI
10.1007/s10815-024-03080-2