title: Automatic blastomere recognition from a single embryo image.
abstract: The number of blastomeres of human day 3 embryos is one of the most important criteria for evaluating embryo viability. However, due to the transparency and overlap of blastomeres, it is a challenge to recognize blastomeres automatically using a single embryo image. This study proposes an approach based on least square curve fitting (LSCF) for automatic blastomere recognition from a single image. First, combining edge detection, deletion of multiple connected points, and dilation and erosion, an effective preprocessing method was designed to obtain part of blastomere edges that were singly connected. Next, an automatic recognition method for blastomeres was proposed using least square circle fitting. This algorithm was tested on 381 embryo microscopic images obtained from the eight-cell period, and the results were compared with those provided by experts. Embryos were recognized with a 0 error rate occupancy of 21.59%, and the ratio of embryos in which the false recognition number was less than or equal to 2 was 83.16%. This experiment demonstrated that our method could efficiently and rapidly recognize the number of blastomeres from a single embryo image without the need to reconstruct the three-dimensional model of the blastomeres first; this method is simple and efficient.
authors.affiliation: College of Information Science and Technology, BNU, Beijing 100875, China.
authors.forename: Yun
authors.identifier: 0000-0001-5574-2325
authors.initials: Y
authors.lastname: Tian
authors.affiliation: College of Information Science and Technology, BNU, Beijing 100875, China.
authors.forename: Ya-bo
authors.initials: YB
authors.lastname: Yin
authors.affiliation: College of Information Science and Technology, BNU, Beijing 100875, China.
authors.forename: Fu-qing
authors.initials: FQ
authors.lastname: Duan
authors.affiliation: Assisted Reproductive Medical Center, Navy General Hospital, Beijing 100048, China.
authors.forename: Wei-zhou
authors.initials: WZ
authors.lastname: Wang
authors.affiliation: Obstetrics and Gynecology Department, Navy General Hospital, Beijing 100048, China.
authors.forename: Wei
authors.initials: W
authors.lastname: Wang
authors.affiliation: College of Information Science and Technology, BNU, Beijing 100875, China.
authors.forename: Ming-quan
authors.initials: MQ
authors.lastname: Zhou
mesh_terms: D000465:Algorithms; D001331:Automation; D001757:Blastomeres / Q000502:physiology*; D049109:Cell Proliferation; D004622:Embryo, Mammalian / Q000502:physiology*; D006801:Humans; D007091:Image Processing, Computer-Assisted; D015203:Reproducibility of Results