Deep Learning-based Video Object Detection for Single-and Multi-Cell Analysis and Evaluation in Time-Lapse Imaging

Background : In this paper, we prove the efficiency of a video object detection algorithm through deep learning to have the most essential video of time-lapse data for the completion of artificial intelligence vision object detection architecture that is used for prediction purpose. We alsoinvestigated time-lapse video data, which is the most important part since it recorded during in vitro fertilization process. Particularly, to achieve the most efficient video object detection by limiting special-purpose object detection to only medical healthcarebio-domains, all conditions were satisfied among the single-stage videoobject detection architectures, and proved as theoretical proofs and experiments. Method: Due to the characteristics of bio-medical in the experimental purpose, we applied artificial intelligence neural networks as a way to capture the frames per second (fps)changes of time-varying video images. To gain advantages in science and mathematics in the biomedical domain, we considered the aspects of entropy, confidence, and object occurrence probability. Accurate time-lapse video object detection factors include: ( i ) first, the accuracy of the number of cells divided after embryo fertilization, ( ii ) second, the acute cell size during cell division, ( iii ) third, the morphological uniformity of embryos, and ( iv ) fourth, the possibility of possible fertilization after cell division. Results : The most significant finding in this study is the accurate counting of cells after embryo fertilization, as detected through time-lapse video object recognition. From an AI vision perspective, we propose a fast and efficient video detection framework by implementing and evaluating two distinct learning models: RetinaNet, a single-stage detector, and Fast R-CNN, a multi-stage detector. Their performance was compared against other deep learning-based detection models. Theoretical insights and practical implications regarding the full cycle of human embryonic development were derived, particularly through the identification and prediction of abnormal temporal patterns.